Master Of Software Engineering (Artificial Intelligence Advanced)

Our rigorous, 64-week Master of Software Engineering programme is for experienced software engineers looking to significantly deepen their knowledge and pursue specialist careers in the field of AI. Unlike a traditional degree, the Master of Software Engineering is tailored to your unique needs and creative challenges. You’ll have the opportunity to investigate advanced software engineering techniques, challenge the foundational principles of your chosen discipline and explore the boundaries of software design, all under the expert guidance of one-on-one professional mentors and our industry-leading faculty.

Key Study Outcomes:

Course Delivery

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Workload and Assessment

Typical assessment includes:

Project/Application/Research Proposal, Process/Research Documentation, Application Outcome, Reflective Journal/Blog, Report/Essay, Presentation/Pitch, Examinations/Tests/Quizzes, Research, Collaboration, Individual self-directed major project, Work integrated learning project work, Software development for social enterprise

Subject Information

In this subject the students are introduced to the main project management principles and modern software project management practices. During the subject, the different methods for managing and optimising the software development process are discussed along with the different techniques for performing each phase of the software development life cycle.

In this subject students are introduced to the current Software Engineering standards and processes, with the aim of enabling them to analyse, design, and implement software projects that follow certain quality measures at every stage of the Software Development Life Cycle. The subject covers requirements engineering, modelling and design of software, software architecture, verification and validation of software systems, and other topics that are related to software engineering practices.

The intended aim of this subject is to equip students with fundamentals of Secure by Design and enable abstraction of its underlying key principles. The course content is oriented towards the core pillars of Information Security: Confidentiality, Integrity and Availability. The subject is structured around the main Secure Development Lifecycle (SDLC) Models, Security by Design principles, appropriate SDLC model selection, application of secure development techniques, vulnerabilities and techniques to tackle, secure design and development best practices, introduction to encryption, introduction to the classification of security flaws and application security.

This subject helps students explore several important fields of general inquiry pertaining to significant intellectual issues related to human beings so they can view everyday problems and formulate solutions in new ways. Broadly, the subject covers the theory of knowledge, human cognition, ethical and moral values, analysis of human history, critical analysis, appreciation of literature and arts and social interaction among human beings through a technological context. Human Centered Design is to give students an appreciation of the factors that influence human behavior and interactions so that they can apply specialised skills to help solve problems that affect diverse societies.

This subject aims to give a broad introduction of intelligent systems, that is, how technologically advanced machines perceive and respond to the world around them. Discussion will focus on how Artificial Intelligence (AI) concepts and classifications are used to design intelligent systems. Overview of AI topics such as representation, reasoning, search methods, intelligent agents, learning, uncertainties and probabilities, perception and action, and communication are presented. It also includes discussions of AI classifications such as Machine Learning, Robotic, Natural Language Processing, Speech Recognition, Expert Systems, Computer Vision, and how they are used to make intelligent systems. This subject also enables students to understand the particular ethical issues that AI presents and how it can be used to benefit society.

This subject introduces students to a framework for developing good scholarly inquiry skills and fundamental knowledge needed to make rational decisions about research strategies. Students will be presented with research strategies to critically investigate exemplar studies and examine the connection between a research question with appropriate research design and methodology. On completion of this subject, students should be able to develop researchable questions, and write research proposals and literature reviews. They will have a critical understanding of the strengths and limitations of the quantitative, qualitative and mixed method approaches to research. They will also learn about the ethical principles of research, challenges in getting approval and the approval processes.

The purpose of this subject is to provide a solid mathematical background that students will encounter in studies of Artificial Intelligence, specifically in sub-areas such as machine learning, natural language processing, speech recognition, and computer vision. This subject will cover topics including linear algebra (equations, functions and graphs), differentiation and optimisation, vectors and matrices, statistics and probabilities. Thorough understanding of these mathematical concepts is necessary for understanding the inner workings of the algorithms in Artificial Intelligence.

This subject is designed to give a graduate-level student an in-depth understanding of the methodologies, technologies, mathematics and algorithms currently used in machine learning. Students will learn the theory behind a range of machine learning tools and practice applying the tools to different applications. It covers topics such as classification, linear models, learning theory, generative models, graphical models and learning paradigm. This subject covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor. Students are given short programming assignments that include hands-on experiments using machine learning algorithms and methods.

This subject builds on the skills and knowledge students acquired from Machine Learning Principles and focuses on deep learning. It introduces students to foundational topics on neural networks, its applications to sequence modelling, computer vision, generative models and reinforcement learning. Focus will be given on learning how to model and train neural networks to implement a variety of computer vision applications. Students will be presented with practical examples of how to develop applications using deep learning.

Knowledge in programming and understanding of machine learning concepts is required in this subject.

This subject is designed to provide students an opportunity to pursue a significant project in a professional environment related to their specialisation. This enables students to develop skills that enhance their prospects of gaining meaningful employment and build their career for the future.
Work integrated learning broadens the students’ learning environment while they are studying and allows them to see first-hand how their learnings in their degree translates in practice, as well as how ‘real world’ practice relates to what they are learning at University.

Students enrolled in Masters (Advanced) have an opportunity to avail one of the three options below simultaneously for this subject and “Advanced Technology – Work Integrated Learning”.

There are three options available to students:

Option 1: Industry Placement

Students are offered the opportunity to work within a technology company as an intern or volunteer at a technology non-profit organisation. It encourages students to build long-term relationships with the tech industry and provides an opportunity for them to work with and learn from people who may end up becoming colleagues, managers or mentors. It also provides a context in which to enhance their communication skills and work collaboratively in a professional arena. Students will undertake a series of industry-led tasks that are relevant to their field of study in order to understand the key concepts of working in and managing a professional technology team with emphasis placed on the operation of the environment.

Option 2: Industry Live Brief

Industry live brief, also known as an industry project engages students in an activity where the parameters of success are set by the client. Academic staff and industry provide supervision for students, while industry provides, mentorship in addition. Numerous technology firms have ideas and opportunities they would like to explore and prototype; this is where students or student teams connect with industry to achieve scale with minimal risk.

An understanding of research methodologies appropriate to professional practice and the documentation of personal creative investigation is explored. Students also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The subject fosters a cross-specialisation perspective and draws on both specialised and common software engineering practices.

Students are required to work both independently and as part of a collaborative team that includes industry representatives to conduct research, analyse and define project parameters and deliver innovative solutions that expand the notion of an industry live brief.

Options 3: Capstone

Students execute, finalise and present their self-initiated project exhibiting a sophisticated understanding of software engineering, whilst addressing the university ethos. Central to the project will be evidence of critical analysis and reflexive and reflective practice, social engagement, in addition to the use of refined visual language in its execution with particular industry relevancy for which their project is intended. Students draw upon the philosophical, practical, methodological, theoretical and technical tools they have gathered over the duration of the degree to complete a successful project. Students are mentored through this research project by an industry supervisor with complementary practice-based research expertise. Projects must pertain to the field of software engineering and in particular to their specialisation.

Students are required to work independently or as part of a collaborative team in order to conduct research, analyse and define project parameters and deliver innovative solutions.

This subject is designed to provide students with professional experience in an area related to their specialisation. The aim of providing industry-specific opportunities is to enable students to develop skills that will enhance their prospects of gaining meaningful employment and building their career for the future.
Much of the benefit of work integrated learning comes from observation, practicing under supervision and reflection. Work Integrated Learning is an excellent way to broaden the students learning environment while they are studying. It allows them to see first-hand how what they are learning in their degree translates into practice, as well as how ‘real world’ practice relates to what they are learning at University.
This subject will develop work ready skills and boost students’ employability while they are studying.

There are two work integrated learning options available to students:
Option 1: Industry Placement
Students are offered the opportunity to work within a technology company as an intern or volunteer at a technology non-profit organisation. It encourages students to build long-term relationships with the tech industry and provides an opportunity for them to work with and learn from people who may end up becoming colleagues, bosses or mentors. It also provides a context in which to enhance their communication skills and work collaboratively in a professional arena. Students will undertake a series of industry-led tasks that are relevant to their field of study in order to understand the key concepts of working in and managing a professional technology team with emphasis placed on the operation of the environment.

Option 2: Industry Live Brief
This subject requires students to respond to criteria set within the context of an Industry Live Project. An understanding of research methodologies appropriate to professional practice and the documentation of personal creative investigation will be explored. Students will also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The subject is delivered from a cross specialisation perspective and draws on both specialised and common software engineering practices.
Students are required to work both independently and as part of a collaborative team in order to conduct research, analyse and define project parameters and deliver innovative solutions that expand the notion of an industry live brief.

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