Skill up, retrain and gain vital new knowledge from some of the world's best teachers, with our new short online programs offered as part of the Australian Government’s higher education relief package for workers who have been impacted by COVID-19.
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.
This program is designed to be delivered online and completed full-time over Semester 2 2020.
Students will complete four (4) courses over this period:
- COMP6730 Programming for Scientists
- COMP6670 Introduction to Machine Learning
- ENGN6528 Computer Vision
- ENGN6627 Robotics
For more information please visit Programs & Courses
Apply now for Semester 2 2020. Applications close 6 July 2020.
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.
Higher education relief package and fees
This short online program is offered as part of the Australian Government’s higher education relief package for workers who have been impacted by COVID-19.
This program is only open to domestic students who will be eligible for a Commonwealth Supported Place (CSP).
Fees are available at a subsidised rate of $2,500 (for 4 courses) for 2020. Only courses enrolled in Semester 2 2020 will receive the subsidised rate. Any courses enrolled in from 2021, will revert to the standard CSP rates.
Fees are determined and charged at the individual course level.
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. 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.
Prerequisites and prior knowledge
For the Graduate Certificate of Machine Learning and Computer Vision, students require basic knowledge of programming and mathematics in linear algebra.
You do need to make a strong commitment on the courses to follow the requirements and learn computing skills.
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.
Applications for Semester 2 2020 close Monday 6 July 2020.
Successful applicants must accept their offer by Wednesday 22 July 2020.
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 email@example.com.
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.