This two-year Master of Machine Learning and Computer Vision (MMLCV) program provides students with specific knowledge and prepares them with competitive professional skills and high flexibility to build their career in the field of Machine Learning and Computer Vision. ANU is one of the finest research universities in Australia, and hosts the ARC Centre of Excellence for Robotic Vision. This new program will be offered by world-class prominent professors and researchers in Computer Vision, Machine Learning, and Artificial Intelligence, based in the College of Engineering and Computer Science (CECS). For interested students, this program also provides a potential pathway to PhD study.

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.

Learning Outcomes

1. Understand computer vision and visual perception problems and propose and develop novel solutions based on current research literature and state-of-the-art computer vision techniques.
2. Proficiently apply development tools for solving computer vision and machine learning problems.
3. Present the methodologies and implementation details in a concise and clear manner.
4. Conduct concept design, implementation, experimental analysis and testing consistent with current practice in computer vision and machine learning, including standard metrics and benchmark datasets.
5. Apply advanced knowledge, techniques and tools to real-world computer vision and machine learning applications. 

At a minimum, all applicants must meet program-specific academic/non-academic requirements, and English language requirements. Admission to most ANU programs is on a competitive basis. Therefore, meeting all admission requirements does not automatically guarantee entry. 

• A Bachelor degree or international equivalent in a cognate disciplines with a GPA of 5/7. Or:
• A Bachelor degree or international equivalent in a cognate discipline with a GPA of 4/7 and a minimum of three years relevant work experience. 

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.

In line with the university's admissions policy and strategic plan, an assessment for admission may include competitively ranking applicants on the basis of specific academic achievement, English language proficiency and diversity factors. 

• TOEFL: Paper-based score: 570 (min. 4.5 in TWE). Internet-based score: 80 (min. 20 reading and writing, 18 in speaking and listening
• IELTS Academic:  6.5 overall (min. 6.0 in each subtest)
• PTE Academic: 64 overall (min. 55 in each of the communicative skills)

• Cambridge CAE: 176 overall (min.169 in each subtest)

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The Australian National University

Acton Campus,

ACTON,

Australian Capital Territory,

2601, CANBERRA, Australia