AIMS

The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications.

INDICATIVE CONTENT

Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Generics

AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.

INDICATIVE CONTENT

• Logic: Propositional and predicate logic, resolution proofs, mathematical proof
• Discrete mathematics: Sets, functions, relations, order, well-foundedness, induction and recursion
• Automata: Regular languages, finite-state automata, context-free grammars and languages, parsing
• Computability briefly: Turing machines, computability, decidability

A functional programming language will be used to implement and illustrate concepts.

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques.

INDICATIVE CONTENT

• The dangers of destructive update
• Functional programming
• Recursion
• Strong type systems
• Parametric polymorphism
• Algebraic types
• Type classes
• Defensive programming practice
• Higher order programming
• Currying and partial application
• Lazy evaluation
• Monads
• Logic programming
• Unification and resolution
• Nondeterminism, search, and backtracking

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

In this subject students examine the implications of the digitisation of data, information, and communications on organisations and society. Students will investigate how digitisation affects individuals, organisations, and society with associated security, compliance, legal and regulatory considerations. These implications are also examined in regard to ethical questions around information privacy, accessibility, ownership, and accuracy.

INDICATIVE CONTENT

Topics covered may include the impact of new and emerging information products and services on social networks, on privacy, censorship and content control, information security, intellectual property, citizenship, and other aspects of organisational and daily life.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

Computers are invaluable tools for modelling and simulating complex systems in a range of real word domains. The complex behaviours exhibited by many biological, social and technological systems - such as epidemics, urban systems and robotics - challenge our ability to predict, analyse and design such systems. Building computational models of these systems can help us better understand their structure and behaviour, and make better decisions about their design and control.

The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement and evaluate computational models of complex systems.

INDICATIVE CONTENT

Topics covered will be selected from:

• the use of models for science, engineering and policy
• dynamical systems analysis
• complexity and emergent behaviour
• agent-based models
• design, communication and evaluation of models
• analysis and visualisation of model behaviour
• case study exemplars of specific types of models, such as:
• • spatial models (eg, transportation)
  • network models (eg, epidemics)
  • adaptive models (eg, robotics)

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains.

Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis.

This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms.

This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software).

INDICATIVE CONTENT

• Cluster computing: elements of parallel and distributed computing, cluster systems architecture, resource management and scheduling, single system image, parallel programming paradigms, cluster programming with MPI
• Utility computing: foundations and grid computing technologies
• Cloud computing: cloud platforms, Virtualization, Cloud Application Programming Models (Task, Thread, and MapReduce), Cloud applications, and future directions in utility and cloud computing
• "Big data" processing and analytics in distributed environments.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

Mathematical modelling is important for understanding and engineering many facets of complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.

INDICATIVE CONTENT

Topics covered will be selected from: Deterministic and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; complex networks; simulation and analysis of complex systems; concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking.

AIMS

The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications.

INDICATIVE CONTENT

Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Generics

AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.

INDICATIVE CONTENT

• Logic: Propositional and predicate logic, resolution proofs, mathematical proof
• Discrete mathematics: Sets, functions, relations, order, well-foundedness, induction and recursion
• Automata: Regular languages, finite-state automata, context-free grammars and languages, parsing
• Computability briefly: Turing machines, computability, decidability

A functional programming language will be used to implement and illustrate concepts.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles.

INDICATIVE CONTENT

The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation.

AIMS

Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques.

INDICATIVE CONTENT

• The dangers of destructive update
• Functional programming
• Recursion
• Strong type systems
• Parametric polymorphism
• Algebraic types
• Type classes
• Defensive programming practice
• Higher order programming
• Currying and partial application
• Lazy evaluation
• Monads
• Logic programming
• Unification and resolution
• Nondeterminism, search, and backtracking

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

To construct a software system, requirements must be analysed and modelled, and designs developed and evaluated; this subject teaches knowledge and skills needed for these tasks. This includes the development of static and dynamic models for aspects of both the problem space and the solution space. The emphasis here is on an Agile approach, and on techniques appropriate for object-oriented development.

INDICATIVE CONTENT

Topics covered include:

• Analysis and modelling requirements
• Developing, modelling and evaluating designs
• Modelling using the Unified Modelling Language (UML)
• Software design processes and principles
• Common design patterns and software architectures
• Tools for design and development

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles.

INDICATIVE CONTENT

The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation.

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources.

This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures with OpenMP, distributed memory architectures with MPI, and GPU accelerators with CUDA.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

Mathematical modelling is important for understanding and engineering many facets of complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.

INDICATIVE CONTENT

Topics covered will be selected from: Deterministic and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; complex networks; simulation and analysis of complex systems; concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking.

AIMS

The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications.

INDICATIVE CONTENT

Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Generics

AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

This subject introduces a range of information security consulting services typically provided by leading management consultants in industry. The subject will cover the fundamental principles and practice of security risk assessment, incident response and disaster recovery, knowledge leakage, systems and network security, and policy and culture. Students will develop an appreciation for the kinds of consulting services that can be developed and marketed to industry in each of these areas. Consulting techniques in proposal writing, pricing, and marketing to prospective clients will also be discussed.

This subject supports course-level objectives by allowing students to have in-depth knowledge of the specialist area of information security management. The subject’s assessment tasks include the writing of a comprehensive consulting proposal and research into critical security issues faced by organisations. These tasks will encourage students to work in a team to develop a high-level of achievement in writing, research activities, and presentation skills.

Students who have a weighted average mark of at least 75% in the Master of Information Systems have the option to complete the on-line Advanced Elective ISYS90090 Cyber Security Management instead of ISYS90070 Information Security Consulting.

INDICATIVE CONTENT

Security principles and techniques discussed are: Models for understanding knowledge leakage, Security Risk Assessment Methods including OCTAVE, Firewall and VPN security scenarios, SANS Incident Response Methodology. Real world cases will be drawn from a range of organisation types including critical infrastructure installations in Australia.

AIMS

Software is present in almost every part of our lives, and continues to change the world. Of importance to users is that software is correct, secure, reliable and efficient. The scale and complexity of most software ensures that achieving these qualities is non-trivial. This subject introduces students to the software engineering principles, processes, tools and techniques for analysing, measuring and developing correct, secure, and reliable software.

The subject is one of the foundation subjects for the MC-ENG Master of Engineering (Software) and (Software with Business).

INDICATIVE CONTENT

Topics covered may include: methods for static and dynamic software testing; software security, quality and dependability; reliability measurement and engineering; performance measurement and engineering;software problem analysis and fault isolation; and software engineering tools.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

AIMS

The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities.

INDICATIVE CONTENT

The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls.

AIMS

High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems.

INDICATIVE CONTENT

Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing.

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

AIMS

The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities.

INDICATIVE CONTENT

The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems.

INDICATIVE CONTENT

Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing.

AIMS

The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications.

INDICATIVE CONTENT

Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Generics

AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles.

INDICATIVE CONTENT

The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation.

AIMS

Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques.

INDICATIVE CONTENT

• The dangers of destructive update
• Functional programming
• Recursion
• Strong type systems
• Parametric polymorphism
• Algebraic types
• Type classes
• Defensive programming practice
• Higher order programming
• Currying and partial application
• Lazy evaluation
• Monads
• Logic programming
• Unification and resolution
• Nondeterminism, search, and backtracking

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

The Internet, World Wide Web, bank networks, mobile phone networks and many others are examples for Distributed Systems. Distributed Systems rely on a key set of algorithms and data structures to run efficiently and effectively. In this subject, we learn these key algorithms that professionals work with while dealing with various systems. Clock synchronization, leader election, mutual exclusion, and replication are just a few areas were multiple well known algorithms were developed during the evolution of the Distributed Computing paradigm.

INDICATIVE CONTENT

Topics covered include:

• Synchronous and asynchronous network algorithms that address resource allocation, communication
• Consensus among distributed processes
• Distributed data structures
• Data consistency
• Deadlock detection
• Lader election, and
• Global snapshots issues.

AIMS

The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains.

Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis.

This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms.

This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software).

INDICATIVE CONTENT

• Cluster computing: elements of parallel and distributed computing, cluster systems architecture, resource management and scheduling, single system image, parallel programming paradigms, cluster programming with MPI
• Utility computing: foundations and grid computing technologies
• Cloud computing: cloud platforms, Virtualization, Cloud Application Programming Models (Task, Thread, and MapReduce), Cloud applications, and future directions in utility and cloud computing
• "Big data" processing and analytics in distributed environments.

AIMS

The subject aims to introduce students to parallel algorithms and their analysis. Fundamental principles of parallel computing are discussed. Various parallel architectures and programming platforms are introduced. Parallel algorithms for different architectures, as well as parallel algorithms addressing specific scientific problems are critically analysed.

INDICATIVE CONTENT

Topics include: principles of parallel computing, PRAM model, PRAM algorithms, parallel architectures, OpenMP, shared memory algorithms, systolic algorithms, parallel communication patterns, PVM/MPI, scientific applications, hypercube, graph embeddings and extended parallel computing models.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources.

This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures with OpenMP, distributed memory architectures with MPI, and GPU accelerators with CUDA.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications.

INDICATIVE CONTENT

Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Generics

AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

New interaction technologies continuously expand the range of input and output methods available in human-computer interaction. Interaction is no longer limited to desktop computers, windows-based interfaces, or keyboards and mice. Interfaces now include tangible communication, mobile and ubiquitous devices, ambient displays and sensing in public spaces. Novel interactions require specific methods to enable their conception, design, evaluation and use in creating interactive systems. This subject will introduce a selection of different interaction media and examine the specific methods used to create interactive systems with them. Underlying these specific methods are general conceptual approaches to design that are focussed on innovative or disruptive interactions between users and technology. Case studies will cover both fundamental research and industrial design practice. An emphasis is placed on developing the skills to critique and adapt different interface technologies and paradigms, to develop prototype systems, and evaluate new interactions to ensure that they meet their intended goals.

This subject follows a flipped classroom model. This means that the lectures are delivered online and class time is used for practical activities and active learning tasks.

User Experience (UX) means the way we respond to technology, including our practical, intellectual, emotional and affective responses. UX is widely recognised as a major determinant of successful technology outcomes, and it provides the design inspiration behind some of the most successful innovations in digital technologies that define the present era. This subject concerns the methods and techniques that are used to identify what characterises UX and how you can recognise, measure and evaluate it in a variety of contexts. This entails a deep understanding of the psychological and social theories underlying UX, combined with practical knowledge of the various industry methods and tools currently in use. In terms of practice, an emphasis is placed on learning the skills needed to design, justify and conduct appropriate evaluations, and the interpretation of findings. In terms of theory, special emphasis is placed on how to identify and evaluate the various facets of UX, across a range of social and work-based settings, and across a range of technologies.

This subject introduces students to the theories and methods used to understand people and settings for designing technical systems. The subject will equip students with the knowledge and skills needed to gather information about people and activities, to understand the intended users of the systems, and to use the insights gained from this process to identify design requirements. This subject is for students interested in a career in user experience (UX) design, interaction design, service design, usability engineering, and human-computer interaction research. It will be of value to students aiming to work in all areas of information technology development and implementation.

AIMS

The Web has radically changed society, politics, science, business and the way people work. This subject introduces the concepts, technologies and standards underpinning the World Wide web and its applications. You will learn to apply tools and techniques required to model, design and develop applications for the web that can run on one or more platforms. Topics covered include the infrastructure of the web; the architecture of web applications; data representation and structure of the web; modeling and development processes for Web applications; security and social aspects of the Web. This subject assumes background programming skills and the basics of algorithmic thinking. These skills are combined with incremental and iterative development to develop functional and creative web applications that can support specific requirements or aspects of human work or social behaviour.

INDICATIVE CONTENT

Fundamental aspects of the Web: client server model, modelling of web applications (modelling data, content, functional aspects and navigation), incremental and iterative design and development of web applications, usability aspects and testing of web applications, and web application security.

Examples of Web applications that students develop are:

• A location-aware application for finding recommended restaurants nearby
• A social app for hosting and developing HTML5 games
• An application that lets users upload photos of themselves to see what they’d look like with different hairstyles

Information architecture encompasses the processes for investigating and designing the interfaces for large-scale information systems. It involves planning and creating the search methods and browsing mechanisms that users will exploit to discover the information that they need. This subject will introduce a range of methods for discovering the ways in which users conceptualize the structure of the information that they are trying to navigate and discover, as well as theories on how information is organised. The subject explains how to analyse data about an information system’s use and from that analysis create concrete models of both cognitive and information behaviour. These models will be used to inform effective designs for discovery tools. Evaluation methods for testing the effectiveness of information discovery tools will also be taught. Good information architecture is the lynch-pin for modern information systems, from corporate websites to online libraries and public services. Throughout the subject, theory and practice will be closely interconnected, and design decisions will have to be justified with both empirical evidence and fundamental principles from information theory and science.

Social Computing is a field of study that investigates computing techniques and systems to support, mediate, and understand aspects of social behaviours. Understanding the principles and foundations of Social Computing is important because of the rapid proliferation of social systems, particularly those aimed at end-users (e.g. social networking websites, crowd sourcing platforms, knowledge sharing platforms, etc.). This subject will introduce you to key concepts and principles of Social Computing, and provide you with training to investigate how these systems influence human behaviours, how to improve current implementations, and how to identify ways to better support social activities and interactions.

This subject involves in-depth investigation of a significant problem related to Human-Computer Interaction or a related discipline. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. Under the supervision and guidance of an academic researcher, students are required to design and conduct a research investigation. This would typically involve a literature review, experimentation and data collection, and data analysis. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

This subject offers an overview of major health informatics research areas and methods that contribute to quality improvement, scientific research, and technological innovation in healthcare and biomedicine. The subject sets out the scientific foundations of digital health, and disciplined approaches to understanding the implications of digital health for health system performance.

The subject is arranged in blocks of study that examine methods for: (a) Undertaking digital health research and innovation projects, including: justifying a project in pragmatic and conceptual terms; drawing on existing practice and knowledge; specifying and staging work packages; meeting needs for partnerships and resources; assuring socially and ethically responsible conduct; reporting on progress rigorously and communicating for impact; (b) Managing exponential growth in health and biomedical knowledge, including: increasing openness in research data life cycle management; automating processes of generating, synthesising, and translating evidence; assuring the quality of electronic decision support systems for clinicians and patients; producing sophisticated forecasts and scenarios of the future of health; (c) Analysing structured and unstructured health data, including: wrangling phenome, exposome and other omics data; scaling up clinical, translational and population health research on platforms; approaching artificial intelligence in medicine through data analytics techniques and machine learning; (d) Modelling and simulating the dynamics of health conditions and health services, including: building personalised and population-level models of health and disease; mapping patient journeys, clinical workflows, and health supply chains; creating immersive environments for healthcare system learning and research.

Information architecture encompasses the processes for investigating and designing the interfaces for large-scale information systems. It involves planning and creating the search methods and browsing mechanisms that users will exploit to discover the information that they need. This subject will introduce a range of methods for discovering the ways in which users conceptualize the structure of the information that they are trying to navigate and discover, as well as theories on how information is organised. The subject explains how to analyse data about an information system’s use and from that analysis create concrete models of both cognitive and information behaviour. These models will be used to inform effective designs for discovery tools. Evaluation methods for testing the effectiveness of information discovery tools will also be taught. Good information architecture is the lynch-pin for modern information systems, from corporate websites to online libraries and public services. Throughout the subject, theory and practice will be closely interconnected, and design decisions will have to be justified with both empirical evidence and fundamental principles from information theory and science.

Social Computing is a field of study that investigates computing techniques and systems to support, mediate, and understand aspects of social behaviours. Understanding the principles and foundations of Social Computing is important because of the rapid proliferation of social systems, particularly those aimed at end-users (e.g. social networking websites, crowd sourcing platforms, knowledge sharing platforms, etc.). This subject will introduce you to key concepts and principles of Social Computing, and provide you with training to investigate how these systems influence human behaviours, how to improve current implementations, and how to identify ways to better support social activities and interactions.

This subject can only be taken following successful completion of INFO90008 Human-Computer Interaction (HCI) Project with a score of 75 or above, and provides students the ability to conduct a substantial and in-depth capstone project. The subject involves in-depth investigation of a significant problem related to Human Computer Interaction or a related discipline. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. Under the supervision and guidance of an academic researcher, students are required to design and conduct a substantial research investigation. This would typically involve an extensive literature review, meticulous experimentation and data collection, and thorough data analysis. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

AIMS

This subject focuses on how Knowledge Management (KM) and a range of Information Technologies and analysis techniques are used to support KM initiatives in organisations. Technologies likely to be considered are: collaborative and social media tools; corporate knowledge directories; data warehouses and other repositories of organizational memory; business intelligence including data-mining; process automation; workflow and document management. The emphasis is on high-level decision-making and the rationale of technology-based initiatives and their impact on organizational knowledge and its use. This subject supports course-level objectives by allowing students to develop analytical skills to understand the complexity of real-world KM work in organisations. It promotes innovative thinking around the deployment of existing and emerging information technologies for KM. The subject contributes to the development of independent critical inquiry, analysis and reflection.

INDICATIVE CONTENT

Techniques of analysis and design likely to be learned are: critical thinking, discourse analysis and design thinking. Real-world case studies in the form of fieldwork are conducted likely from the following domains: software industry; retail; creative/fashion industry; manufacturing; emergency management. Real case-study work will shape thinking about IT support for KM in these industries.

Healthcare is information intensive. Health data are generated, shared, consumed, and stored in a variety of partially overlapping complex networks. Healthcare lags behind many other sectors, despite efforts to use digital technologies to shape and improve health data and information processes since the middle of the 20th Century. The need for digital transformation of health is driven by socio-economic concerns (making healthcare more accessible and affordable) and patient safety (reducing medical errors, and redundant and ineffective interventions).

This subject introduces the background, current state, and future opportunities of digital health. It provides a basic understanding of health and disease and how individuals experience both. It explores the nature of biomedical data, information, and knowledge - and how digital technologies are shaping the way these are used. Digital health technologies are examined from ethical, historical, technological, and psycho-social perspectives, considering positive and negative impacts.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications.

INDICATIVE CONTENT

Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Generics

AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

AIMS

In this subject, students will learn about the principles, concepts and design strategies used in the development of Spatial Data Infrastructure (SDI) as an enabling platform to facilitate multi-sourced data and service discovery, access, integration and use. An example of SDI is the land titles system and the tools used to maintain and interrogate it. Emphasis will be placed on both technological and institutional factors that facilitate the development of SDIs. Students will examine related disciplines such as land and marine administration as well as technical areas such as interoperability, web-mapping and web-delivery to better meet sustainable development objectives. This subject is of particular relevance to students who want to pursue a career in spatial data management, land administration, but is also relevant to a range of geomatic engineering disciplines that use and produce large spatial datasets for decision-making in support of sustainable development.

The subject partners with other subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry.

INDICATIVE CONTENT

SDI concepts and theory, current SDI initiatives, SDI development strategies and development models; SDI as an enabling platform, SDI and Spatially Enabled Government and Society, SDI and partnership approaches, financing and capacity building, challenges for developed and developing countries, capacity building, marine SDI and seamless SDI, policy and privacy Issues, SDI and land administration, metadata, standards and clearinghouses, SDI application areas, and SDI implementation and benchmarking.

AIMS

Spatial databases are fundamental to any geographical information system. Efficient and effective representation and retrieval of spatial information is a non-trivial task. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data.

This subject builds on a student’s knowledge of computer programming, databases, and spatial information. Students who successfully complete this subject may find professional employment in designing, implementing, customising and maintaining databases for the increasingly wide range of spatial software applications.

INDICATIVE CONTENT

Fundamentals of spatial databases; spatial data modelling in relational databases, including vector, raster, and network data; spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees.

AIMS

Much of the world’s data relate to processes and objects situated in space. This spatial dimension of the data requires special representation and analytical approaches. Therefore, application problems such as the analysis, monitoring and simulation of Smart cities and smart environments cannot be handled by standard programming approaches and require specialist knowledge.

Using case studies in the domains of smart environments and smart cities, this subject will enable students to learn the necessary computational thinking approaches and acquire technical software development skills to address specific spatial information problems enabling them to effectively address Spatial Data Science problems.

The subject will focus on the application of state-of-the art programming techniques and applications of spatial analytics to solve a series of spatial data science use cases, in particular in the urban informatics domain. The course projects will also introduce the principal aspects of software development life cycle relevant for a data scientist.

This subject assumes students are familiar with elementary spatial information data and the varied ways these are used by various stakeholders. Fundamental understanding of a programming language is assumed, with the first few weeks of the semester providing an ability to acquire these skills (using Python).

Students who successfully complete this subject will have a distinct competitive advantage in the smart environment, smart cities, and urban analytics practices, with the ability to support consultancy work requiring computational data handling, analysis, and the development of software tools for spatial analysts beyond the traditional spatial information industry.

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

This subject provides an overview of a wide range of issues relating to infrastructure engineering, with a particular focus on the environmental, economic and social implications of engineering projects. Students will gain a holistic understanding of the complexities of – and potential trade-offs in – decision-making, including considerations of social equity, quality of life and wellbeing, and assessment of economic and environmental impacts. Students will learn about the influential role that infrastructure plays in shaping a society, and the effects both short-term and long-term. Students will also learn to apply various methods to evaluate infrastructure projects from a sustainability perspective. Lectures will be complemented by examples or case studies, assigned tasks and a group project in order to consolidate and apply learnings. Throughout the term, students will be supported to enhance their research skills as well as their oral and written communication skills.

This subject is part of a trio of subjects that consider different aspects of infrastructure projects. Engineering Site Characterisation explores how to determine the character of a site for an infrastructure project. Sustainable Infrastructure Engineering examines how a project relates to the broader social, economic, and environmental context. Engineering Project Implementation concentrates on the operational aspects of implementing a project.

AIMS

The aim of this course is to provide students with an introduction to urban traffic engineering and transport planning principles. General theory as well as analytical techniques for solving common transport engineering problems is presented.

The key theme in this course is how to improve the sustainability of transport systems. This includes understanding and predicting travel demand. This course emphasizes techniques for modelling and evaluating schemes based on environmental, health and social outcomes. Behavioural choice modelling methods are used to predict demand for public transport and non-motorised transport modes.

CVEN90048 Transport Systems provides a transport-specific learning experience that relates to, builds-on, and extends from the skills and competencies developed via the following Civil Engineering subjects: CVEN90043 Sustainable Infrastructure Engineering and CVEN90045 Engineering Project Implementation.

INDICATIVE CONTENT

Topics covered include:

• Introduction to Transport Systems
• Traffic Flow Theory
• Traffic Control Devices
• Unsignalised Intersection Capacity Analysis
• Travel Surveys
• Sustainability
• Traffic Survey Methods
• Public Transport
• Transport Network Models
• Road Safety
• Signalised Intersection Capacity and Timing
• Freeway Management
• Geometric Design of Roads

This subject has been integrated with the Skills Towards Employment Program (STEP) and contains activities that can assist in the completion of the Engineering Practice Hurdle (EPH).

AIMS

Information Technology now impacts on people and processes within and beyond organisational boundaries. The discipline of Information Systems is concerned with the effective use of IT by people and organisations. This subject provides context on Information Systems practice and use viewed through a range of roles that interact with these systems, including those of system developers, users, business managers, IT managers, and vendors. It provides students with a foundation that is further built on in other information systems subjects.

The subject supports course-level objectives by allowing students to understand the complexity of real-world applications of information systems within a range of industries. It challenges students to integrate concepts, theories and frameworks with case studies and examples drawn from industry. The emphasis is on gaining a tool kit for a rich understanding of the practical problem solving rather than learning the theory per se. The subject contributes to the development of independent critical inquiry, case study analysis and problem solving.

INDICATIVE CONTENT

Klings’s Social Informatics, Prahalad and Hamel’s Core Competencies, Porter’s Competitive Advantage, Chan and Luftman’s Concepts of Business – IT Alignment, Cullen and Seddon’s Outsourcing Management, Willcock’s Offshoring Challenges, Agarwal and Sambamurthy’s IT Governance issues and various Change Management Models.

AIMS

As with many other forms of technology, information technologies have lifecycles ranging from initial conception, to possible adoption, and widespread use, to eventual obsolescence.

This subject will examine emerging information technologies and the issues that relate to them, including: how they evolve and, enter usage, and their likely future effects on people and social structures.

INDICATIVE CONTENT

The subject provides an understanding of both technical and managerial issues, as well as strategic implications of emerging technologies and issues. Upon completion of the subject, students should be able to (a) understand key enabling technologies and become an effective participant in technology-enabled business endeavours and initiatives; (b) recognise ways of leveraging the technology to improve intra and inter-organisational processes and enhance a firm’s competitive position; (c) gain skills for building careers and taking advantage of entrepreneurial opportunities through emerging technologies, and (d) understand the factors that influence how relevant an emerging technology will be in the long run.

AIMS

This subject focuses on how Knowledge Management (KM) and a range of Information Technologies and analysis techniques are used to support KM initiatives in organisations. Technologies likely to be considered are: collaborative and social media tools; corporate knowledge directories; data warehouses and other repositories of organizational memory; business intelligence including data-mining; process automation; workflow and document management. The emphasis is on high-level decision-making and the rationale of technology-based initiatives and their impact on organizational knowledge and its use. This subject supports course-level objectives by allowing students to develop analytical skills to understand the complexity of real-world KM work in organisations. It promotes innovative thinking around the deployment of existing and emerging information technologies for KM. The subject contributes to the development of independent critical inquiry, analysis and reflection.

INDICATIVE CONTENT

Techniques of analysis and design likely to be learned are: critical thinking, discourse analysis and design thinking. Real-world case studies in the form of fieldwork are conducted likely from the following domains: software industry; retail; creative/fashion industry; manufacturing; emergency management. Real case-study work will shape thinking about IT support for KM in these industries.

AIMS

In this subject students will learn about the foundations of spatial data and their analysis. Emphasis will be placed on learning how to investigate the patterns that arise as a result of processes that may be operating in space. For example, students will learn to identify geographic clusters of disease cases, or hotspots of crime. A variety of scientific tools including probability theory, combinatorics, descriptive statistics, distributions and matrix algebra will be taught. Students will learn essential skills that are fundamental for all applications of geographic information.

The subject partners with other subjects on spatial data management and visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. Spatial Analysis builds on the fundamental knowledge of probability and statistics, mathematics, as well as computer literacy to write simple algorithms, and the preparation and management of data for sophisticated analysis software.

INDICATIVE CONTENT

Spatial autocorrelation, spatial data structures and algorithms, point patterns, measures of dispersion, measures of arrangements, line and network analysis, patterns of areas and in fields, and the role of spatial scale and spatial aggregation problems.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIM

This subject involves the in-depth investigation of a significant problem related to Spatial IT. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is fundamentally a research-based project, giving a capstone experience and piece of scholarship to students.

INDICATIVE CONTENT

The student will develop a research question in spatial information technology and an appropriate research methodology for investigating the question. After approval by the supervisor(s) the student will apply this methodology, analyse results, and report in a thesis.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject introduces the concepts of Geographic Information Systems (GIS) and its application in landscape architecture, urban planning and development. It will:

• introduce the origin and development of GIS respect to landscape architecture, urban planning and development;
• introduce basic GIS concepts, data structure, data format, and data management;
• introduce fundamental GIS operations such as digitising, overlay analysis, spatial analysis, hydrological analysis, 3D analysis, etc.;
• address key issues of applying GIS in planning, design and development, such as landscape capacity and suitability analysis, urban heat island analysis, water sensitive urban design, property management, etc.;
• place how GIS will facilitate site analysis, inform decision making and improve efficiency and productivity in planning, design and development.

The subject will be delivered through lectures/guest lectures, lab tutorials, workshops and practical sessions synthesising dominant themes in this fields of using GIS as tool to achieve sustainable design and ecological landscape planning.

SUBJECT NOTE : In 2021, this subject is taught online and on campus (dual delivery) . To allow for this the student needs the following:

Software Requirement: ESRI ArcGIS 10.7 will be used. Students can request ArcGIS 10.7 via the online chat service 'Ask a librarian' https://library.unimelb.edu.au/contact_the_library#chat (available during library opening hours). Students will be provided license code and instructions for download and installing the software on their own computer.

Hardware Requirement: ESRI has recommended hardware requirements. Specification of hardware requirements can be found at (https://desktop.arcgis.com/en/system-requirements/10.7/arcgis-desktop-system-requirements.htm)

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

Research is a process of acquiring new knowledge by systematically and rigorously applying methods to address well-formulated questions. To be valuable, new knowledge must address a significant theoretical question, it must be supported by evidence and be able to stand up to critical scrutiny, and its presentation to other researchers and/or to the public must be persuasive. This subject is an introduction to research thinking, skills and methodologies as they apply to computing and related disciplines. The subject will foster the development of critical thinking, a sceptical and rigorous approach, and awareness of research ethics. This subject will be particularly useful for students contemplating undertaking a research degree, or for students currently enrolled in a research degree (MPhil or PhD) or a course-work degree with a research project (MIT, MIS).

INDICATIVE CONTENT

Research skills covered will include: surveying relevant literature, developing productive research questions, selecting and designing appropriate methods, analysing data and reasoning about their theoretical implications, communicating research both in writing and through oral presentation, and understanding the ethics of research. Qualitative methods covered include: ethnography, field data collection techniques (interviews, focus groups), thematic analysis, case studies and design-based research. Quantitative methods covered include: statistical thinking and techniques, hypothesis testing, experiment design, survey design, simulation studies.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

There is a need for civil engineers to increase their knowledge and skills in freight systems since they are actively involved in the planning, design, construction, maintenance and management of a range of freight infrastructure such as roads, bridges and ports. Civil engineers require expertise in freight systems to reduce the social and environmental costs from freight including safety, noise and emissions. Training in freight systems also provides opportunities for freight networks to become more productive and efficient increasing economic benefits for society.

Freight infrastructure allows the freight system to operate, facilitating vital components of our economy, including production, distribution and trade.

The purpose of the freight system relates to its role in providing a service for the economy. Freight transport is a derived demand; it does not exist for its own sake. The primary demand is for the consumption of goods where there is spatial separation. Goods are generally stored, processed and consumed at different locations. There is a need for goods to move to increase their value for producers, manufacturers and consumers. Freight can be considered as the economy in motion. Goods are transported as part of the economic activities of production, manufacturing and consumption.

INDICATIVE CONTENT

Freight networks provide a service for producers and manufacturers allowing access to markets for the consumption of goods. The benefit of goods being transported relates to their increased value at their trip destination. Reduced transport operation costs leads to lower production and distribution costs that creates opportunities for lower priced goods.

In the past few years, the Architecture Engineering and Construction (AEC) industry has observed the evolution of simple 2D drafting programs into integrated Building Information Modelling (BIM) based on 3D spatial technologies. In this subject, students will learn how BIM is used to model, store and visualise architectural, structural, and facilities components of an infrastructure in 3D. Students will also learn how adding time and cost information to BIM allows AEC to foster collaboration in designing infrastructures, minimize the risk of construction errors and optimise the maintenance of them.

The subject is of particular relevance to students wishing to establish a career in civil engineering, property management, surveying, spatial information and urban planning but is also relevant to a range of disciplines where 3D building information should be considered.

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

To introduce students to the techniques and technology of remote sensing: the extraction of information from satellite and airborne image data. This subject assumes prior knowledge of image processing techniques such as that acquired in subjects such as GEOM30009 Imaging the Environment. Students passing this subject will have the skills to work under supervision in a spatial information or remote sensing agency of consultancy providing services, for example, to natural resource managers.

INDICATIVE CONTENT

Use of image processing systems. High level digital image processing, correction and classification; applications of remote sensing in the geosciences, engineering, and resource assessment and inventory; image data in geographic information systems. Detailed application studies in emergency/disaster management, environmental assessment and geological mapping.

AIMS

In this subject students will learn the theory and applications of Global Navigation Satellite Systems (GNSS), such as the Global Positioning Systems (GPS). The subject focuses on high precision GNSS, their design and fundamental operational characteristics, strengths and weaknesses, error sources and mitigation, measurement and data processing techniques. It is a pre requisite for the subject GEOM90039 Advanced Surveying and Mapping. The subject is of broad relevance to students with an interest in technology or to those specifically wishing to establish a career in engineering, mining or cadastral surveying, but is also relevant to a range of mapping, spatial, land surveying and civil engineering disciplines where the capture and processing of spatial or survey measurements to meet a specific performance specification should be considered.

INDICATIVE CONTENT

High precision GPS surveying, Global Navigation Satellite Systems, GPS measurements, Differential GPS, GPS reference station networks, GPS errors, ellipsoidal heights, geodetic datum, geoid, GPS data processing.

NOTE: An intensive learning period of approximately 3-4 days will be conducted as part of this subject. The exact dates and venue will be confirmed at the start of the subject.

AIMS

To introduce students to advanced imaging technologies and the methods for extracting quantitative information from multi-source imagery. This subject builds on the knowledge of subjects such as imaging the environment, by considering multi-source images of the target to provide additional information such as the distance from the target to object from which a three dimensional representation can be constructed. It also considers imaging of targets where illumination is provided by the instrument rather than natural light reflection or radiation from the target. Students who successfully complete this subject may find work in a variety of remote sensing or specialist consultancies or agencies. The techniques learnt may also be applied to other industries such as quality control in manufacturing or recording of archaeological sites.

INDICATIVE CONTENT

The subject covers the characteristics of specialised imaging techniques and instruments including LIDAR, photogrammetry, and high resolution satellite imagery, as well as processing techniques for generating products such as orthoimages and digital terrain models. It also discusses considerations, inherent errors, and limitations of each of these techniques.

AIMS

In this subject students will learn about the range of computational techniques applicable to problems commonly arising in surveying and spatial information. This subject applies the mathematical and computational knowledge acquired in COMP20005 Engineering Computation; MAST10007 Linear Algebra (or its equivalent). The content of this subject is relevant to GEOM90033 Satellite Positioning Systems, and GEOM90039 Advanced Surveying and Mapping. The subject is of particular relevance to students wishing to establish a career in surveying engineering, mining, mapping, or spatial information in general, and is also relevant to a range of civil engineering disciplines where the capture and processing of spatial or survey measurements to meet a specific performance specification should be considered.

INDICATIVE CONTENT

Least squares adjustment, survey measurement errors, survey network design and adjustment, coordinate systems, geodetic datum, datum transformations.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.

INDICATIVE CONTENT

• Logic: Propositional and predicate logic, resolution proofs, mathematical proof
• Discrete mathematics: Sets, functions, relations, order, well-foundedness, induction and recursion
• Automata: Regular languages, finite-state automata, context-free grammars and languages, parsing
• Computability briefly: Turing machines, computability, decidability

A functional programming language will be used to implement and illustrate concepts.

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques.

INDICATIVE CONTENT

• The dangers of destructive update
• Functional programming
• Recursion
• Strong type systems
• Parametric polymorphism
• Algebraic types
• Type classes
• Defensive programming practice
• Higher order programming
• Currying and partial application
• Lazy evaluation
• Monads
• Logic programming
• Unification and resolution
• Nondeterminism, search, and backtracking

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

In this subject students examine the implications of the digitisation of data, information, and communications on organisations and society. Students will investigate how digitisation affects individuals, organisations, and society with associated security, compliance, legal and regulatory considerations. These implications are also examined in regard to ethical questions around information privacy, accessibility, ownership, and accuracy.

INDICATIVE CONTENT

Topics covered may include the impact of new and emerging information products and services on social networks, on privacy, censorship and content control, information security, intellectual property, citizenship, and other aspects of organisational and daily life.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

Computers are invaluable tools for modelling and simulating complex systems in a range of real word domains. The complex behaviours exhibited by many biological, social and technological systems - such as epidemics, urban systems and robotics - challenge our ability to predict, analyse and design such systems. Building computational models of these systems can help us better understand their structure and behaviour, and make better decisions about their design and control.

The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement and evaluate computational models of complex systems.

INDICATIVE CONTENT

Topics covered will be selected from:

• the use of models for science, engineering and policy
• dynamical systems analysis
• complexity and emergent behaviour
• agent-based models
• design, communication and evaluation of models
• analysis and visualisation of model behaviour
• case study exemplars of specific types of models, such as:
• • spatial models (eg, transportation)
  • network models (eg, epidemics)
  • adaptive models (eg, robotics)

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains.

Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis.

This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms.

This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software).

INDICATIVE CONTENT

• Cluster computing: elements of parallel and distributed computing, cluster systems architecture, resource management and scheduling, single system image, parallel programming paradigms, cluster programming with MPI
• Utility computing: foundations and grid computing technologies
• Cloud computing: cloud platforms, Virtualization, Cloud Application Programming Models (Task, Thread, and MapReduce), Cloud applications, and future directions in utility and cloud computing
• "Big data" processing and analytics in distributed environments.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

Mathematical modelling is important for understanding and engineering many facets of complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.

INDICATIVE CONTENT

Topics covered will be selected from: Deterministic and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; complex networks; simulation and analysis of complex systems; concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking.

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.

INDICATIVE CONTENT

• Logic: Propositional and predicate logic, resolution proofs, mathematical proof
• Discrete mathematics: Sets, functions, relations, order, well-foundedness, induction and recursion
• Automata: Regular languages, finite-state automata, context-free grammars and languages, parsing
• Computability briefly: Turing machines, computability, decidability

A functional programming language will be used to implement and illustrate concepts.

AIMS

Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles.

INDICATIVE CONTENT

The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation.

AIMS

Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques.

INDICATIVE CONTENT

• The dangers of destructive update
• Functional programming
• Recursion
• Strong type systems
• Parametric polymorphism
• Algebraic types
• Type classes
• Defensive programming practice
• Higher order programming
• Currying and partial application
• Lazy evaluation
• Monads
• Logic programming
• Unification and resolution
• Nondeterminism, search, and backtracking

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

To construct a software system, requirements must be analysed and modelled, and designs developed and evaluated; this subject teaches knowledge and skills needed for these tasks. This includes the development of static and dynamic models for aspects of both the problem space and the solution space. The emphasis here is on an Agile approach, and on techniques appropriate for object-oriented development.

INDICATIVE CONTENT

Topics covered include:

• Analysis and modelling requirements
• Developing, modelling and evaluating designs
• Modelling using the Unified Modelling Language (UML)
• Software design processes and principles
• Common design patterns and software architectures
• Tools for design and development

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles.

INDICATIVE CONTENT

The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation.

AIMS

Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

INDICATIVE CONTENT

Topics include:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations.

Topics include:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

AIMS

This subject involves students undertaking professional work experience at a Host Organisation’s premises. Students will work under the supervision of both a member of academic staff and an external supervisor at the Host Organisation.

During the period of work experience, students will be introduced to workplace culture and be offered the opportunity to strengthen their employability. Students will undertake seminars covering topics that will include professional standards of behaviour and ethical conduct, working in teams, time management and workplace networking.

This subject aims to give participants theoretical frameworks, practical insights, and preliminary skills to work in their chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem.

Participants will develop these understandings, insights and skills by working in teams on a strategically-important innovation challenge sponsored by an industry organisation. This subject is similar to Creating Innovative Engineering (ENGR90034), but is designed for students seeking a multi-disciplinary learning experience.

Participants will learn theories of human-centred innovation and apply them in their project. They will learn how to work in teams and use those skills to deliver the project. They will learn to conceptualise their career as an innovation project, and how to apply the innovation skills and theories presented in the subject to their own careers.

The subject is challenging, experiential and requires significant self-direction.

Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website.

All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project.

AIMS

Information Visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (database subjects such as GEOM90018 Spatial Databases), and the processing of data (data analytics subjects such as GEOM90006 Spatial Analysis). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design.

INDICATIVE CONTENT

Fundamentals of information visualisation and data graphics; human perception; foundations of graphical user interface design; cartographic design; geovisualisation; exploratory visual spatial data analysis; evaluation of information visualisation interfaces.

AIMS

This is an introductory subject to Geograhpic Information Systems (GIS) and Geographic Information Science, both practically and theoretically, at postgraduate level. Spatial information is ubiquitous in decision making. Be it in urban planning, in traffic or disaster management, in way-finding, in issues of the environment, public health and sustainability, or in economic contexts: the question of 'where' is a fundamental one. Spatial information is also special in many respects, such as its dimensionality and autocorrelation, its volume, its links to the Internet of Things (things are always located somewhere), to social networks (which exist in space and time), to streaming data from sensors everywhere, or to intelligent (location-aware) systems. The subject provides the foundations for more specialized subjects on spatial data management, spatial data analysis and spatial data visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. It is also suited for every postgraduate student who is looking for solid GIS skills.

INDICATIVE CONTENT

We will discuss representations and analysis of this information in spatial information technologies, from location-based services to geographic information systems. Topics addressed are observing the environment; spatial and spatiotemporal data representations, spatial analysis and spatial communication. The practical part will introduce to GIS in a hands-on manner, starting in individual software training and then applying new skills in a team-designed GIS project.

The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources.

This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures with OpenMP, distributed memory architectures with MPI, and GPU accelerators with CUDA.

This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects.

AIMS

Mathematical modelling is important for understanding and engineering many facets of complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.

INDICATIVE CONTENT

Topics covered will be selected from: Deterministic and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; complex networks; simulation and analysis of complex systems; concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking.

AIMS

The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

INDICATIVE CONTENT

Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services.

AIMS

The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions.

This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology.

INDICATIVE CONTENT

The subject will be made up of three parts:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

AIMS

This subject introduces a range of information security consulting services typically provided by leading management consultants in industry. The subject will cover the fundamental principles and practice of security risk assessment, incident response and disaster recovery, knowledge leakage, systems and network security, and policy and culture. Students will develop an appreciation for the kinds of consulting services that can be developed and marketed to industry in each of these areas. Consulting techniques in proposal writing, pricing, and marketing to prospective clients will also be discussed.

This subject supports course-level objectives by allowing students to have in-depth knowledge of the specialist area of information security management. The subject’s assessment tasks include the writing of a comprehensive consulting proposal and research into critical security issues faced by organisations. These tasks will encourage students to work in a team to develop a high-level of achievement in writing, research activities, and presentation skills.

Students who have a weighted average mark of at least 75% in the Master of Information Systems have the option to complete the on-line Advanced Elective ISYS90090 Cyber Security Management instead of ISYS90070 Information Security Consulting.

INDICATIVE CONTENT

Security principles and techniques discussed are: Models for understanding knowledge leakage, Security Risk Assessment Methods including OCTAVE, Firewall and VPN security scenarios, SANS Incident Response Methodology. Real world cases will be drawn from a range of organisation types including critical infrastructure installations in Australia.

AIMS

Software is present in almost every part of our lives, and continues to change the world. Of importance to users is that software is correct, secure, reliable and efficient. The scale and complexity of most software ensures that achieving these qualities is non-trivial. This subject introduces students to the software engineering principles, processes, tools and techniques for analysing, measuring and developing correct, secure, and reliable software.

The subject is one of the foundation subjects for the MC-ENG Master of Engineering (Software) and (Software with Business).

INDICATIVE CONTENT

Topics covered may include: methods for static and dynamic software testing; software security, quality and dependability; reliability measurement and engineering; performance measurement and engineering;software problem analysis and fault isolation; and software engineering tools.

AIMS

The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects.

INDICATIVE CONTENT

Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools.

This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills.

The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD.

Enrolment in this subject requires a weighted average mark of 75 or above.

Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability.

By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found.

AIMS

This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software.

INDICATIVE CONTENT

Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team.

AIMS

This subject involves an in-depth innovation investigation under the supervision of a member of the academic staff and in the context of the University's engagement initiatives. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication.

INDICATIVE CONTENT

Indicative content includes innovation methodology, customer & problem discovery, customer & problem validation, innovation experiments, and innovation presentations.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

AIMS

The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities.

INDICATIVE CONTENT

The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls.

AIMS

High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems.

INDICATIVE CONTENT

Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing.

This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

Note: this subject is available as an intensive subject during the Summer and Winter semesters, and as a semester-long subject during Semesters 1 and 2. For the semester-long subject students will be required to attend a virtual weekly meeting with the host organisation. The meeting will occur on either a Wednesday or a Friday afternoon (AEST), time to be determined by the host organisaton. Students must be available for both time periods, even though you will only attend meet with the company during one of the time periods. This is to enable allocation to a suitable project.

This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project.

AIMS

Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context.

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

AIMS

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

AIMS

At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing.

INDICATIVE CONTENT

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

AIMS

As we become more dependent on networks in every aspect of our lives the task of protecting those networks becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can automate the analysis of such data to better detect and predict security incidents and vulnerabilities within our networks and organisations.

INDICATIVE CONTENT

The subject will first introduce the types of data sources that are relevant to detecting different types of security threats in practice. Indicative examples are operating system logs, web server logs, packet traces, flow records and deep packet inspection traces. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.

Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.

AIMS

The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities.

INDICATIVE CONTENT

The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls.

This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access.

This subject will be made up of three parts:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering.

Students must seek subject coordinator approval to enrol in the subject and must contact subject coordinator before week 1 to discuss enrolment.

Students must identify their own volunteer experience and have completed this experience before the semester begins.

Students who have completed significant volunteer experience or open-source contributions, in an information technology or information systems capacity, not undertaken within an MSE organised internship, cannot receive credit for this subject.

To participate in this subject, a student must first demonstrate completion of one of the following:

1. Software: Unpaid development of publicly available, open source, based on an extracurricular or independent activity, including working on a volunteer basis with a non-profit organisation

2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome

3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities

The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes:

1. Software: The student must provide information about the application or other relevant artefact, and temporal information about release updates, codebase size, server logs, app store statistics, etc.

2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes

3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities

The portfolio will include a reflective component to enable students to consider the completed project both from the point of view of the project itself, as well as the volunteer experience.

All work must be verifiable as that of the student, and that the work was unpaid/voluntary and not part of any paid work or internship. Evidence of the student contribution is required.
The module accredits volunteer work that has been completed during the time that the student is enrolled in their course, and students can only enrol with the approval of the subject coordinator, after delivery of a draft portfolio in the first two weeks of the semester.

AIMS

From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.

INDICATIVE CONTENT

Topics covered may include:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition