Graduate Certificate of Machine Learning and Computer Vision

Graduate Certificate of Machine Learning and Computer Vision

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Build the robots and computers of tomorrow, today

Discounted fees available. Commonwealth Supported Places on offer for domestic students in 2021 (see Fees)

This new online course will help graduates prepare for a world increasingly driven by artificial intelligence and robotics. Studying this program will help you gain the skills needed to build the robots and computers of tomorrow, today.

"AI and automation are changing our world in many profound ways," said convener Dr Miaomiao Liu.

"From chat bots, to self-driving vehicles to drones that deliver our food or help farmers manage their crops, just to name a few examples. And the foundation of this new technology is machine learning, computer vision and robotics.”

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 Graduate Certificate provides a pathway to the Master of Machine Learning and Computer Vision and other Masters programs.

This program is available for domestic students only. 

Program outline

This program is designed to be delivered online and completed part-time in 2021.

Students will complete four (4) courses over this 1-year period:

For more information please visit Programs & Courses

The next intake for applications is Semester 1 2022.


Study plan

Example recommended study plan:

Semester 1

Semester 2

Information about learning remotely

All materials are delivered remotely. You will not be required to come to Canberra.

You will need to be available during specified times to participate in remote tutorials and labs. Several tutorial times will be offered and you can attend your nominated session.

Estimated workload for each course is 130 hours of study over the course. This includes (virtual) attendance at classes and working on assessments.

Admission and eligibility

This program is available for domestic students only. 

Current ANU students are not eligible for this program. If you have previously been an ANU student and have completed that program, then you may apply for this Graduate Certificate. 

A Bachelor degree or international equivalent with a minimum GPA of 4/7 in a cognate discipline. 

Cognate disciplines include: Electrical and/or Electronics engineering, Computer Science, Software Engineering, Computer Engineering, Automation, Mechatronics, Telecommunications, Mathematics, Physics, Bioinformatics, Control systems and engineering, Statistics, Artificial Intelligence, Biomedical Science, Optical Engineering.

Unfortunately, there are no alternative pathways for prospective students who do not meet the minimum GPA requirement.

All applicants must meet the University’s English Language Admission Requirements for Students.

Fees and Commonwealth Supported Places

Applicants offered a place to study in this Graduate Certificate commencing their studies in 2021 will be eligible for a Commonwealth Supported Place (CSP) under the Job Ready Graduate scheme.  This fee will apply to courses completed in this Graduate Certificate within 2021 and the first half of 2022, allowing you to study part-time. If you do not complete the degree by mid-2022 the remainder of your subjects will be billed on a full-fee basis from Semester 2 (Winter/Spring) 2022.

Fees are determined and charged at the individual course level. 

More information:

How to apply

Applications for this program are completed online. Visit Programs and Courses. The black ‘apply’ button on the top right-hand side of the page will take you to a portal where you can lodge your application. 

The next intake for applications is Semester 1 2022. 


Credit is not available for these programs. If you have already completed the undergraduate version of one of the courses listed above, you should contact the program convener to identify a suitable replacement.


Once you have accepted your offer, you will receive an email with permission codes that allow you to enrol in your courses.

Please allow up to two (2) weeks for your acceptance to be processed and these codes to be sent. If you do not receive permission codes within two (2) weeks of accepting your offer, please contact

Students in the Graduate Certificate of Machine Learning and Computer Vision are only permitted to enrol in the four courses listed in the study plan. If you have already completed an undergraduate version of one of these courses, please contact the program convener for guidance on a suitable replacement.

Pathways to Masters

For interested students, this Graduate Certificate may be used as a pathway to other ANU postgraduate programs.

A Graduate Certificate is classed as an AQF 8 qualification. In many cases, completing a Graduate Certificate with a GPA of 5/7 will meet the academic entry requirements of most Masters programs. However, it may not necessarily meet a program’s cognate requirement.

As a general guide, students who complete the Graduate Certificate of Machine Learning and Computer Vision at ANU may be eligible for the following credit in our Master programs:*

  • Master of Computing: admission with GPA of 5/7 and 18 units credit.
  • Master of Applied Data Analytics: admission with GPA of 5/7 and 6 units credit.
  • Master of Machine Learning and Computer Vision: admission with GPA of 5/7 and will meet cognate requirement. 24 units credit.
  • Master of Engineering (all): students will still require a Bachelor degree in a cognate area for admission. Students could use a GPA of 5/7 in the Graduate Certificate for entry, if the GPA from their Bachelor qualification does not meet the academic requirement.
    • Master of Engineering in Mechatronics: 24 units credit.
    • Master of Engineering in Electrical Engingeering: 24 units credit.
    • Other Master of Engineering programs: 12 units credit.

*Please note that this is a guide only. All credit and exemption requests are assessed on a case-by-case basis based on academic background and personal circumstances.


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Updated:  10 August 2021/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing