Admission Requirements
This program is only open to Domestic students
A Bachelor degree or international equivalent with a minimum GPA of 4.0 / 7.0 in a cognate discipline.
All applicants must meet the University’s English Language Admission Requirements for Students
Program Indicative Fees
This program is only open to domestic students.
Commonwealth Supported Places (CSPs) are available for this program.
Indicative CSP fees $3,976
Students unsuccessful in securing a CSP can study as a DTF student.
Domestic Tuition Fees (DTF) $17,640
Commonwealth Supported Places (CSPs) for this program are limited, competitive and awarded on academic merit. There is no application process for a CSP. All students commencing in Semester 1 2021 will be ranked according to the GPA of the qualification used as the basis of admission.
For more information see: https://www.anu.edu.au/students/program-administration/fees-payments/hecs-help
Students unsuccessful in securing a Commonwealth Supported Place (CSP) can undertake the program as domestic tuition full fee (DTF) paying students.
For more information on Domestic Tuition fees click here .
Credit Granted
Credit for this program is not available.
Cognate Disciplines
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.
Fee Information
All students are required to pay the Services and amenities fee (SA Fee)
The annual indicative fee provides an estimate of the program tuition fees for international students and domestic students (where applicable). The annual indicative fee for a program is based on the standard full-time enrolment load of 48 units per year (unless the program duration is less than 48 units). Fees for courses vary by discipline meaning that the fees for a program can vary depending on the courses selected. Course fees are reviewed on an annual basis and typically will increase from year to year. The tuition fees payable are dependent on the year of commencement and the courses selected and are subject to increase during the period of study.
For further information on Fees and Payment please see: https://www.anu.edu.au/students/program-administration/fees-payments
Scholarships
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.
The Graduate Certificate of Machine Learning and Computer Vision comprises the equivalent of 0.5 years of full-time study. The program is taught online in intensive blended mode. Students are expected to be enrolled part-time and can complete the program in 1 year of part time study.
This program is available to domestic students only.
NOTE: Commonwealth Supported Places (CSPs) are available for this program.
Domestic 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. Costs are indicated on the Admissions and Fees Tab.
Career Options
ANU ranks among the world's very finest universities. Our nearly 100,000 alumni include political, business, government, and academic leaders around the world.
We have graduated remarkable people from every part of our continent, our region and all walks of life.
Learning Outcomes
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
Inherent Requirements
Information on inherent requirements for this program is currently not available.
