Master of Artificial Intelligence (Online)
Course code: MC-AIMO
June, August, October
AUD $30,480 (2026 indicative first year fee). Commonwealth Supported Places (CSPs) are not available
June, August, October
AUD $30,480 (2026 indicative first year fee)
IELTS 6.5: with no band less than 6.0
Course structure
Overview
100% online flexible learning
Our online Master of Artificial Intelligence is designed to fit your life. You’ll study one subject at a time, giving you more breaks throughout the year and helping you achieve deeper focus.
Study where and when it suits you, so you’re in control.
Structure:
- 12 subjects (150 credit points)
- 8 core subjects
- 2 elective stream subjects
- 2 pathway-focused subjects
- Graduate in as soon as 24 months.
Elective streams
Hone your AI expertise with two stream options aligning to your career goals.
Technology stream
Develop the technical foundations behind tomorrow’s AI innovations in the technology stream. You’ll learn to design advanced algorithms, engineer AI agents and build the systems powering emerging technologies.
This stream is ideal if you’re motivated by innovation and want to turn technical ideas into real-world AI solutions.
Application stream
Apply AI to organisational contexts through the application stream. In this stream you will learn how to deploy AI responsibly, make informed strategic decisions and translate complex business challenges into practical AI solutions.
This stream suits strategic thinkers who want to lead AI initiatives and deliver measurable impact.
Pathways
After completing your core and elective stream subjects, you will have the choice of completing the coursework or research pathway.
Coursework pathway
Delivered through two hands-on, project-based capstone subjects, the coursework pathway provides the opportunity to design and implement AI solutions. This pathway aims to build your technical confidence and leadership capability needed for AI-focused roles across diverse industries.
Research pathway
In this track, you will undertake an impactful research project focused on an AI-related problem of your choice.
This pathway is intended to support progression toward a PhD in artificial intelligence.
Profile
Nir Lipovetzky
Associate Professor Nir Lipovetzky is the Course Director of this course. His research focuses on artificial intelligence planning, heuristic search, learning, and intention recognition. His work centres on developing novel inference methods for sequential decision-making problems.
A key focus of Professor Lipovetzky’s research connects AI planning with real-world applications, including autonomous systems for agriculture and computational sustainability. He is actively involved in building tools that support education and technology transfer, contributing to open-source platforms such as planning, domains, LAPKT and Planimation.
Nir’s research excellence has been recognised with multiple international awards, including:
- Best dissertation
- Paper and system demonstration prizes at leading conferences such as ICAPS, AAMAS and nternational Planning Competitions
- He was also featured in the IJCAI Early Career Spotlight.
Nir has held major leadership roles in the AI community, including Program and Conference Chair of ICAPS 2019 and 2025.
Explore this course
Explore the subjects you could choose as part of this degree.
The course runs over six online terms per year, and you’ll typically take one subject each term.
Compulsory subjects
Complete all of the following.
| Accordion | |
|---|---|
| AI Programming Fundamentals · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Programming Fundamentals is designed to equip students with essential programming skills and problem-solving techniques tailored for the field of Artificial Intelligence. This subject bridges the gap between fundamental programming concepts and their practical application in AI contexts. By the end of this subject, students will develop efficient algorithms, manipulate large datasets, and implement AI-specific solutions using a modern programming language. |
| Algorithmic Thinking · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Algorithmic Thinking introduces the essential principles and practices 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, strategies must be designed so that the calculations combine effectively. This subject introduces students to the fundamentals of computational efficiency and to many of the classical algorithms and data structures that solve key computational questions. By the end of the subject, students will have developed a robust toolkit for algorithmic thinking, positioning them to approach complex computational problems with confidence and creativity. |
| Machine Learning · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Machine Learning introduce students to the foundations of machine learning and practical skills in data analysis. Students will explore supervised and unsupervised learning algorithms, translating real-world challenges into machine learning tasks. The subject emphasises evaluating model performance using standard metrics and interpreting results. Students will learn to compare different models, considering their strengths and limitations for specific tasks. By the end of the subject, students will acquire skills in designing and implementing machine learning solutions using toolkits, equipping them to contribute to the rapidly evolving field of data science and artificial intelligence and its wide-ranging applications in industry. |
| Foundations of AI · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Foundations of AI introduces students to fundamental concepts, methodologies, and applications of artificial intelligence by applying them to solve real-world problems. This subject bridges the gap between theoretical knowledge and practical implementation, equipping students with the skills to model complex scenarios, apply AI techniques, and communicate results effectively. By the end of the subject, students will have a solid grounding in AI principles and practices, positioning them to engage with more advanced AI topics and applications in their future studies or professional careers. |
| AI Planning for Autonomy · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. This subject introduces the foundations of autonomous agents—intelligent systems that perceive their environment, reason about their goals, and make decisions to act effectively over time. We focus on how such agents can autonomously choose the best course of action in discrete environments, such as planning routes for travel, coordinating transport operations, or scheduling tasks in manufacturing. Students will learn how to represent and solve these problems as sequential decision processes, where each decision impacts future states, actions, and outcomes. The course covers key algorithmic approaches including combinatorial search, automated planning, and reinforcement learning. Emphasis is placed on how these methods enable agents to plan and adapt in dynamic settings. The subject culminates in an open-ended project that challenges students to design intelligent agent behaviours in a realistic scenario—applying the principles and techniques learned throughout the course. |
| Deep Learning and Foundation Models · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Deep Learning and Foundation Models explores the theoretical foundations and practical applications of deep learning and foundation models. Students will critically analyse the mathematical and algorithmic basis of these models, develop hands-on skills in implementing neural architectures using Python, and learn to evaluate and select appropriate models for AI-related problems. By the end of this subject, students will gain a comprehensive understanding of the capabilities and limitations of various deep learning models and their applications in AI. |
| Learning in Multiple Modalities · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Learning in Multiple Modalities explores the fascinating world of multi-modal artificial intelligence, focusing on the computational challenges and techniques associated with processing and analysing various data modalities, including text, images and audio. Students will delve into the theoretical foundations and practical applications of machine learning algorithms designed to handle these diverse data types. By the end of this subject, students will be able to design, implement, and critically evaluate AI systems that can process and analyse multiple data modalities. |
| AI in Society · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI in Society explores the complex interplay between Artificial Intelligence (AI) and society, equipping students with the critical thinking skills necessary to navigate the ethical and social implications of AI technologies. Students will learn to identify potential risks and unintended consequences of AI, while also exploring the positive potential of AI technologies. By the end of the subject, students will be well-prepared to contribute and communicate thoughtfully in discussions on AI ethics and to approach AI development with a keen awareness of its broader societal implications. |
Stream subjects
Complete all of the following.
| Accordion | |
|---|---|
| Autonomy and Optimisation Agents · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Autonomy and Optimisation Agents explores advanced techniques for modelling and controlling dynamical systems. Students will gain a comprehensive understanding of the mathematical principles underlying autonomous agents, including state-space representations, feedback control, and optimisation algorithms. The subject examines key challenges in designing robust autonomous systems, such as handling uncertainty, optimising performance under constraints, and ensuring safe operation in dynamic environments. By the end of this subject, students will be able to design, implement, and evaluate sophisticated autonomous agents capable of operating effectively in real-world, continuous environments. |
| Multi-agent Systems · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Multi-agent Systems delves into the fascinating world of complex systems and agent-based modelling. This subject provides students with a comprehensive understanding of how to analyse, model, and simulate intricate real-world scenarios using computational techniques. By the end of this subject, students will be equipped with the skills to identify key features of complex systems, apply theoretical concepts to real-world problems, select appropriate modelling techniques, and create and validate computational models to analyse the behaviour of complex systems across various domains. |
Coursework pathway subjects
Complete all of the following if you chose this pathway.
| Accordion | |
|---|---|
| AI Research and Development · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Research and Development of AI in Practice challenges students to apply their comprehensive AI knowledge to real-world research and development scenarios. This subject serves as the first part of a capstone experience, bridging theoretical understanding with practical implementation in complex AI projects. Students will identify and analyse AI-related problems, design and justify appropriate research methodologies, and develop implementation plans for innovative AI solutions. Throughout the process, students will engage with relevant stakeholders, considering diverse perspectives and requirements in their project development. By the end of this subject, students will have demonstrated their ability to conceptualise, develop, and critically evaluate AI projects, preparing them for leadership roles in AI research and development in industry settings. |
| AI Implementation and Deployment · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Implementation and Deployment challenges students to apply their comprehensive AI knowledge to real-world implementation and deployment scenarios. This subject serves as the second part of a capstone experience, bridging theoretical understanding and research methodologies with practical implementation and deployment of complex AI projects. Students will implement AI-based solutions, iteratively improve their systems, and evaluate their performance using appropriate metrics. Throughout the process, students will engage with relevant stakeholders, considering technical feasibility, ethical standards, and user needs. By the end of this subject, students will have demonstrated their ability to implement, deploy, and critically evaluate AI systems, preparing them for leadership roles in AI development and deployment in industry settings. |
Research pathway subjects
Complete all of the following if you chose this pathway.
| Accordion | |
|---|---|
| AI Research Project Part A · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Research Project introduces students to advanced research methodologies in Artificial Intelligence, preparing them for doctoral-level research. Students will explore cutting-edge AI research topics, develop skills in formulating research questions, and design rigorous experimental methodologies. This subject aims to bridge the gap between theoretical AI knowledge and practical application, preparing students for real-world challenges in the AI industry. It develops crucial skills in research, project management, communication, and decision-making, while fostering innovation and critical thinking. By simulating authentic industry scenarios and emphasising stakeholder engagement, the subject ensures graduates are well-equipped to lead complex AI projects. This subject is the first part of a two-subject sequence, taught over two consecutive study periods. Students are required to enrol in COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points), consecutively, for a total enrolment of 25 points. Students will receive a ‘CNT’ grade for Part 1. An overall result for the subject is given following completion of the two-subject sequence. Assessment, Subject Intended Learning Outcomes, and Total Time Commitment applies to the entire enrolment across Parts A and B of the subject. The Total Time Commitment for the subject is approximately 400 hours, inclusive of the two study periods [COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points)]. |
| AI Research Project Part B · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Research Project introduces students to advanced research methodologies in Artificial Intelligence, preparing them for doctoral-level research. Students will explore cutting-edge AI research topics, develop skills in formulating research questions, and design rigorous experimental methodologies. This subject aims to bridge the gap between theoretical AI knowledge and practical application, preparing students for real-world challenges in the AI industry. It develops crucial skills in research, project management, communication, and decision-making, while fostering innovation and critical thinking. By simulating authentic industry scenarios and emphasising stakeholder engagement, the subject ensures graduates are well-equipped to lead complex AI projects. This subject builds upon the knowledge and skills acquired in AI Research Project Part A. This subject is the second part of a two-subject sequence, taught over two consecutive study periods. Students are required to enrol in COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points), consecutively, for a total enrolment of 25 points. Students will receive a ‘CNT’ grade for part 1. An overall result for the subject is given following completion of the two-subject sequence. Assessment, Subject Intended Learning Outcomes, and Total Time Commitment applies to the entire enrolment across Parts A and B of the subject. The Total Time Commitment for the subject is approximately 400 hours, inclusive of the two study periods [COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points)]. Refer to COMP90112 AI Research Project Part A for details. |
The course runs over six online terms per year, and you’ll typically take one subject each term.
Compulsory subjects
Complete all of the following.
| Accordion | |
|---|---|
| AI Programming Fundamentals · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Programming Fundamentals is designed to equip students with essential programming skills and problem-solving techniques tailored for the field of Artificial Intelligence. This subject bridges the gap between fundamental programming concepts and their practical application in AI contexts. By the end of this subject, students will develop efficient algorithms, manipulate large datasets, and implement AI-specific solutions using a modern programming language. |
| Algorithmic Thinking · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Algorithmic Thinking introduces the essential principles and practices 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, strategies must be designed so that the calculations combine effectively. This subject introduces students to the fundamentals of computational efficiency and to many of the classical algorithms and data structures that solve key computational questions. By the end of the subject, students will have developed a robust toolkit for algorithmic thinking, positioning them to approach complex computational problems with confidence and creativity. |
| Machine Learning · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Machine Learning introduce students to the foundations of machine learning and practical skills in data analysis. Students will explore supervised and unsupervised learning algorithms, translating real-world challenges into machine learning tasks. The subject emphasises evaluating model performance using standard metrics and interpreting results. Students will learn to compare different models, considering their strengths and limitations for specific tasks. By the end of the subject, students will acquire skills in designing and implementing machine learning solutions using toolkits, equipping them to contribute to the rapidly evolving field of data science and artificial intelligence and its wide-ranging applications in industry. |
| Foundations of AI · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online courses Master of Artificial Intelligence and Graduate Certificate in Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Foundations of AI introduces students to fundamental concepts, methodologies, and applications of artificial intelligence by applying them to solve real-world problems. This subject bridges the gap between theoretical knowledge and practical implementation, equipping students with the skills to model complex scenarios, apply AI techniques, and communicate results effectively. By the end of the subject, students will have a solid grounding in AI principles and practices, positioning them to engage with more advanced AI topics and applications in their future studies or professional careers. |
| AI Planning for Autonomy · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. This subject introduces the foundations of autonomous agents—intelligent systems that perceive their environment, reason about their goals, and make decisions to act effectively over time. We focus on how such agents can autonomously choose the best course of action in discrete environments, such as planning routes for travel, coordinating transport operations, or scheduling tasks in manufacturing. Students will learn how to represent and solve these problems as sequential decision processes, where each decision impacts future states, actions, and outcomes. The course covers key algorithmic approaches including combinatorial search, automated planning, and reinforcement learning. Emphasis is placed on how these methods enable agents to plan and adapt in dynamic settings. The subject culminates in an open-ended project that challenges students to design intelligent agent behaviours in a realistic scenario—applying the principles and techniques learned throughout the course. |
| Deep Learning and Foundation Models · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Deep Learning and Foundation Models explores the theoretical foundations and practical applications of deep learning and foundation models. Students will critically analyse the mathematical and algorithmic basis of these models, develop hands-on skills in implementing neural architectures using Python, and learn to evaluate and select appropriate models for AI-related problems. By the end of this subject, students will gain a comprehensive understanding of the capabilities and limitations of various deep learning models and their applications in AI. |
| Learning in Multiple Modalities · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Learning in Multiple Modalities explores the fascinating world of multi-modal artificial intelligence, focusing on the computational challenges and techniques associated with processing and analysing various data modalities, including text, images and audio. Students will delve into the theoretical foundations and practical applications of machine learning algorithms designed to handle these diverse data types. By the end of this subject, students will be able to design, implement, and critically evaluate AI systems that can process and analyse multiple data modalities. |
| AI in Society · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI in Society explores the complex interplay between Artificial Intelligence (AI) and society, equipping students with the critical thinking skills necessary to navigate the ethical and social implications of AI technologies. Students will learn to identify potential risks and unintended consequences of AI, while also exploring the positive potential of AI technologies. By the end of the subject, students will be well-prepared to contribute and communicate thoughtfully in discussions on AI ethics and to approach AI development with a keen awareness of its broader societal implications. |
Stream subjects
Complete all of the following.
| Accordion | |
|---|---|
| Interactive AI · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Interactive AI equips students with the essential knowledge and skills required to apply Artificial Intelligence (AI) techniques to the design, development, and evaluation of interactive technologies. The subject provides students with a deeper understanding of the role of AI in enhancing user experiences and designing intelligent interactive systems. By the end of the subject, students will be able to apply AI algorithms and methodologies to enhance user experiences, usability, and interaction design, gaining the skills necessary to design and implement AI-driven Human-Computer Interaction (HCI) solutions that meet user needs and preferences through practical projects and hands-on exercises. Students wishing to deepen their knowledge of AI applications are recommended to pair the elective Interactive AI with the elective subject AI Assurance and Trust. |
| AI Assurance and Trust · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Assurance and Trust addresses the growing demand for responsible, reliable, and trustworthy AI systems by integrating foundational knowledge in machine learning with contemporary practices in AI assurance. Students will gain a solid understanding of the mathematical and algorithmic foundations of machine learning, including core principles of learning from data and systems implementation. In parallel, the subject will examine key issues in ensuring that AI systems are robust, interpretable, fair, and aligned with societal expectations. Topics include the detection and mitigation of vulnerabilities (e.g., adversarial attacks and backdoors), management of uncertainty (e.g., out-of-distribution detection), and principles of responsible AI governance, standards, and regulation. By the end of this subject, students will be able to assess, design, and build AI systems that are dependable and trustworthy in real-world contexts. |
Coursework pathway subjects
Complete all of the following if you chose this pathway.
| Accordion | |
|---|---|
| AI Research and Development · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. Research and Development of AI in Practice challenges students to apply their comprehensive AI knowledge to real-world research and development scenarios. This subject serves as the first part of a capstone experience, bridging theoretical understanding with practical implementation in complex AI projects. Students will identify and analyse AI-related problems, design and justify appropriate research methodologies, and develop implementation plans for innovative AI solutions. Throughout the process, students will engage with relevant stakeholders, considering diverse perspectives and requirements in their project development. By the end of this subject, students will have demonstrated their ability to conceptualise, develop, and critically evaluate AI projects, preparing them for leadership roles in AI research and development in industry settings. |
| AI Implementation and Deployment · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Implementation and Deployment challenges students to apply their comprehensive AI knowledge to real-world implementation and deployment scenarios. This subject serves as the second part of a capstone experience, bridging theoretical understanding and research methodologies with practical implementation and deployment of complex AI projects. Students will implement AI-based solutions, iteratively improve their systems, and evaluate their performance using appropriate metrics. Throughout the process, students will engage with relevant stakeholders, considering technical feasibility, ethical standards, and user needs. By the end of this subject, students will have demonstrated their ability to implement, deploy, and critically evaluate AI systems, preparing them for leadership roles in AI development and deployment in industry settings. |
Research pathway subjects
Complete all of the following if you chose this pathway.
| Accordion | |
|---|---|
| AI Research Project Part A · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Research Project introduces students to advanced research methodologies in Artificial Intelligence, preparing them for doctoral-level research. Students will explore cutting-edge AI research topics, develop skills in formulating research questions, and design rigorous experimental methodologies. This subject aims to bridge the gap between theoretical AI knowledge and practical application, preparing students for real-world challenges in the AI industry. It develops crucial skills in research, project management, communication, and decision-making, while fostering innovation and critical thinking. By simulating authentic industry scenarios and emphasising stakeholder engagement, the subject ensures graduates are well-equipped to lead complex AI projects. This subject is the first part of a two-subject sequence, taught over two consecutive study periods. Students are required to enrol in COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points), consecutively, for a total enrolment of 25 points. Students will receive a ‘CNT’ grade for Part 1. An overall result for the subject is given following completion of the two-subject sequence. Assessment, Subject Intended Learning Outcomes, and Total Time Commitment applies to the entire enrolment across Parts A and B of the subject. The Total Time Commitment for the subject is approximately 400 hours, inclusive of the two study periods [COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points)]. |
| AI Research Project Part B · 12.5 pts |
Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Artificial Intelligence. Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term. AI Research Project introduces students to advanced research methodologies in Artificial Intelligence, preparing them for doctoral-level research. Students will explore cutting-edge AI research topics, develop skills in formulating research questions, and design rigorous experimental methodologies. This subject aims to bridge the gap between theoretical AI knowledge and practical application, preparing students for real-world challenges in the AI industry. It develops crucial skills in research, project management, communication, and decision-making, while fostering innovation and critical thinking. By simulating authentic industry scenarios and emphasising stakeholder engagement, the subject ensures graduates are well-equipped to lead complex AI projects. This subject builds upon the knowledge and skills acquired in AI Research Project Part A. This subject is the second part of a two-subject sequence, taught over two consecutive study periods. Students are required to enrol in COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points), consecutively, for a total enrolment of 25 points. Students will receive a ‘CNT’ grade for part 1. An overall result for the subject is given following completion of the two-subject sequence. Assessment, Subject Intended Learning Outcomes, and Total Time Commitment applies to the entire enrolment across Parts A and B of the subject. The Total Time Commitment for the subject is approximately 400 hours, inclusive of the two study periods [COMP90112 AI Research Project Part A (12.5 points) and COMP90113 AI Research Project Part B (12.5 points)]. Refer to COMP90112 AI Research Project Part A for details. |