Program Requirements

The Master of Machine Learning and Computer Vision requires the completion of 96 units.

A minimum of 24 units must come from completion of 8000-level courses.

The 96 units must consist of:

6 units COMP6710 Structured Programming

6 units from completion of a professional practice course from the following list:

COMP6250 Professional Practice: Holistic Thinking and Communication (6 units)

COMP8260 Professional Practice: Responsible Innovation and Leadership (6 units)

  24 units from completion of courses from the following list:

COMP6528 Computer Vision (6 units)

COMP6670 Introduction to Machine Learning (6 units)

COMP8536 Advanced Topics in Deep Learning for Computer Vision (6 units)

COMP8539 Advanced Topics in Computer Vision (6 units)

COMP8600 Statistical Machine Learning (6 units)

COMP8650 Advanced Topics in Machine Learning (6 units)

A further 18 units from completion of Computer Vision and Machine Learning courses in the following list:

COMP6261 Information Theory (6 units)

COMP6262 Logic (6 units)

COMP6320 Artificial Intelligence (6 units)

COMP6490 Document Analysis (6 units)

COMP6528 Computer Vision (6 units)

COMP6670 Introduction to Machine Learning (6 units)

COMP8535 Engineering Data Analytics (6 units)

COMP8536 Advanced Topics in Deep Learning for Computer Vision (6 units)

COMP8539 Advanced Topics in Computer Vision (6 units)

COMP8600 Statistical Machine Learning (6 units)

COMP8610 Computer Graphics (6 units)

COMP8620 Advanced Topics in Artificial Intelligence (6 units)

COMP8650 Advanced Topics in Machine Learning (6 units)

COMP8691 Optimisation (6 units)

ENGN6627 Robotics (6 units)

Either:

COMP6442 Software Construction (6 units); AND

12 units from completion of a research project or industry internship in the following list:

COMP8715 Advanced Computing Team Project that must be taken twice, in consecutive semesters (6+6 units)

COMP8830 Computing Internship (12 units)

AND

12 units of 6000, 7000 or 8000-level coded courses from the subject area COMP or ENGN.

OR

COMP6445 Computing Research Methods (6 units); AND

COMP8800 Advanced Computing Research Project, that must be taken twice, in consecutive semesters (12+12 units)

Students who do not have the approval of an identified supervisor/client for COMP8800 or COMP8830 by week 1 of their penultimate semester will be required to complete COMP8715 to graduate.

12 units of Postgraduate University electives.

Admission Requirements

Applicants must present one of the following:

• A Bachelor degree or international equivalent in a cognate disciplines with a GPA of 5/7
• 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

The GPA for a Bachelor program will be calculated from (i) a completed Bachelor degree using all grades and/or (ii) a completed Bachelor degree using all grades other than those from the last semester (or equivalent study period) of the Bachelor degree. The higher of the two calculations will be used as the basis for admission.

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.

Ranking and English language proficiency: 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. 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. Applicants will first be ranked on a GPA ('GPA1') that is calculated using all but the last semester (or equivalent) of the Bachelor degree used for admission purposes. If required, ranking may further be confirmed on the basis of:

• a GPA ('GPA2') calculated on the penultimate and antepenultimate semesters (or equivalent) of the Bachelor degree used for admission purposes; and/or
• demonstrating higher-level English language proficiency

Prior to enrolment in this ANU program, all students who gain entry will have their Bachelor degree reassessed, to confirm minimum requirements were met.

Further information: English language admission requirements and post-admission support

Diversity factors: As Australia’s national university, ANU is global representative of Australian research and education. ANU endeavours to recruit and maintain a diverse and deliberate student cohort representative not only of Australia, but the world. In order to achieve these outcomes, competitive ranking of applicants may be adjusted to ensure access to ANU is a reality for brilliant students from countries across the globe.

Assessment of qualifications: Unless otherwise indicated, ANU will accept all Australian Qualifications Framework (AQF) qualifications or international equivalents that meet or exceed the published admission requirements of our programs, provided all other admission requirements are also met.

Where an applicant has more than one completed tertiary qualification, ANU will base assessment on the qualification that best meets the admission requirements for the program. Find out more about the Australian Qualifications Framework: www.aqf.edu.au.

Unless otherwise indicated, where an applicant has more than one completed tertiary qualification, ANU will calculate the GPA for each qualification separately. ANU will base assessment on the best GPA of all completed tertiary qualifications of the same level or higher.

ANU uses a 7-point Grade Point Average (GPA) scale. All qualifications submitted for admission at ANU will be converted to this common scale, which will determine if an applicant meets our published admission requirements. Find out more about how a 7-point GPA is calculated for Australian universities: www.uac.edu.au/future-applicants/admission-criteria/tertiary-qualifications.

Alternate Admin Requirements

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.

Employment Opportunities

Machine Learning and Computer Vision have been revolutionising the way we view and interact with the world. The employment opportunities for MMLCV graduates are extensive and span across various industries, reflecting the widespread integration of AI technologies into modern systems and services. The demand for these skills is expected to grow as AI and machine learning continue to drive innovation and transformation across sectors worldwide. Some examples include Data Analyst, Computer Vision Engineer, Machine Learning Engineer, AI Research Scientist, Software Developer, AI Consultant and Startup Founders. Past students have been accepted directly into PhD programs at ANU, University of Queensland, University of Adelaide, University of North Carolina (USA) and Simon Fraser University (Canada).

Learning Outcomes

1. Propose and develop novel solutions based on current research literature and state-of-the-art computer vision techniques. 
2. Research and apply development tools for solving computer vision and machine learning problems.
3. Interpret and present the methodologies and implementation details?relevant to computer vision and machine learning technologies?with audience-?tailored communications.
4. Conduct Machine Learning and/or Computer Vision research (applied or pure) project workflows consistent with current practice.
5. Synthesize advanced knowledge, techniques and tools for computer vision and machine learning applications. 

Inherent Requirements

Information on inherent requirement is currently not available for this program.

Further Information

Machine Learning and Computer Vision (MLCV) is one of the core research strengths of CECC in ANU as host of the ARC Centre of Excellence for Robotic Vision. This program allows students access to all the research-led elements of the current augmentations in MLCV areas and allows them to contribute to the world-leading research expertise in the relevant areas at ANU. Individual and group research and design projects are a strong feature of ANU engineering and computing education, and are integrated into many of the required courses. Currently, computerised visual perception and visual information processing have been used in broad areas such as human-computer interaction, assisted disease diagnosis via medical imaging, visual inspection in mechanical engineering, visual searching in social media etc. In addition, Machine Learning and Computer Vision (MLCV) are prominent research areas in Artificial Intelligence (AI). These areas provide essential research towards the realisation of intelligent and smart cities in the coming decades.