Graduate Certificate in Artificial Intelligence (Online)
Course code: GC-AIMO
June, August, October
AUD $20,320 (2026 indicative first year fee). Commonwealth Supported Places (CSPs) are not available
June, August, October
AUD $20,320 (2026 indicative first year fee)
IELTS 6.5: with no band less than 6.0
Course structure
Overview
100% online flexible learning
Our online Graduate Certificate in 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:
- 4 subjects (50 credit points)
- Graduate in as soon as 8 months.
Develop your AI literacy from the ground up by building confidence through foundational knowledge in algorithms, machine learning and artificial intelligence. As your understanding of AI concepts and IT fundamentals deepens, you will be equipped with the skills to scale ideas into AI applications. This enduring expertise equips you to adapt confidently as AI continues to change.
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. |