single degree

Master of Machine Learning and Computer Vision

A single two year graduate degree offered by the ANU College of Engineering and Computer Science

MMLCV
  • Length 2 year full-time
  • Minimum 96 Units
  • Mode of delivery
    • In Person
  • Field of Education
    • Computer Engineering
  • Academic contact
  • Length 2 year full-time
  • Minimum 96 Units
  • Mode of delivery
    • In Person
  • Field of Education
    • Computer Engineering
  • Academic contact

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 from completion of a programming course from the following list:

COMP6710 - Structured Programming

COMP6730 - Programming for Scientists

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

ENGN6250 - Prof Prac 1

ENGN8260 - Prof Prac 2

          24 units from completion of compulsory courses from the following list:

ENGN6528 - Computer Vision

ENGN8501 - Adv Topics in Computer Vision

COMP6670 - Intro to Machine Learning

COMP8600 - Statistical Machine Learning

24 units from completion of Computer Vision and Machine Learning courses in the following list:

COMP6262 - Logic

COMP6320 - Artificial Intelligence

COMP6490 - Document Analysis

COMP8420 - Neural Networks, Deep Learning

COMP8620 - Advanced Topics in A.I.

COMP8650 - Adv Topics in Machine Learning

COMP8691 - Optimisation

ENGN6627 - Robotics

ENGN8534 - Information Theory

ENGN8535 - Data Analytics

ENGN8536 - Advanced Topics Mechatronics


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

ENGN8602 - Research Project

ENGN6200 - Engineering Internship (3~6 months)


24 units of elective courses offered by the ANU. 


*An extended capstone project or internship up to 24 units of credits may be taken with permission, with the additional units counting toward the 24u of program electives.

Admission Requirements

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. 


Academic achievement & English language proficiency

The minimum academic requirement for full entry and enrolment is a Bachelor degree or international equivalent with a minimum GPA of 5.0/7.0 or 4.0/7.0 (as applicable). 


However, 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.


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

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

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.

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.

Annual indicative fee for domestic students
$36,288.00

For more information see: http://www.anu.edu.au/students/program-administration/costs-fees

Annual indicative fee for international students
$47,880.00

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.

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.

Career Options

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. 

Inherent Requirements

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

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