- Minimum 24 Units
- Academic plan GCMLCV
- CRICOS code NO CRICOS
- UAC code
Mode of delivery
Field of Education
- Artificial Intelligence
This program is available for domestic students only. Current ANU students are not eligible for this program.
A Bachelor degree or international equivalent with a minimum GPA of 4/7.
All applicants must meet the University’s English Language Admission Requirements for Students
This program is only open to Domestic students who will be eligible for a Commonwealth Supported Place (CSP)
For 2020 the fees are at a subsidised rate of $2500 for 4 courses
In 2021 they will revert to standard CSP fee rates
For more information see:
ANU offers a wide range of scholarships to students to assist with the cost of their studies.
Eligibility to apply for ANU scholarships varies depending on the specifics of the scholarship and can be categorised by the type of student you are. Specific scholarship application process information is included in the relevant scholarship listing.
For further information see the Scholarships website.
Rapid societal changes are being driven by the increasing ubiquity of AI and automation. Cornerstone technologies in these fields are Machine Learning and Computer Vision. This program provides students with specific expertise and knowledge in machine learning, computer vision, and robotics. For interested students, this program provides a pathway to complete the Master of Machine Learning and Computer Vision.
This program is available to domestic students only. Current ANU students are not eligible for this program.
Graduates from ANU have been rated as Australia's most employable graduates and among the most sought after by employers worldwide.
The latest Global Employability University Ranking, published by the Times Higher Education, rated ANU as Australia's top university for getting a job for the fourth year in a row.
This program is available for applications until second semester, 2020
Understand computer vision and visual perception problems.
Be proficient in using development tools for solving computer vision and machine learning problems
Present the methodologies and implementation details of an implementation in a concise and clear manner
Apply learned knowledge, techniques and tools to robotics applications