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 Professional Practice 1
ENGN8260 Professional Practice 2
24 units from completion of compulsory courses from the following list:
ENGN6528 Computer Vision
ENGN8501 Advanced Topics in Computer Vision
COMP6670 Introduction to Machine Learning
COMP8600 Statistical Machine Learning
24 units from completion of Computer Vision and Machine Learning courses in the following list:
ENGN8535 Engineering Data Analytics
COMP6490 Document Analysis
ENGN8536 Advanced Topics in Mechatronics -
COMP8420 Neural Networks, Deep Learning and Bio-Inspired Computing
COMP8650 Advanced Topics in Machine Learning
COMP6320 Artificial Intelligence
COMP8620 Advanced Topics in Artificial Intelligence
ENGN8534 Information Theory
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.
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.
English Language Requirements
All applicants must meet the University’s English Language Admission Requirements for Students.
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.
Electrical engineering, Electronics engineering, Computer Science, Software Engineering, Computer Engineering, Automation, Mechatronics, Telecommunications engineering, Mathematics, Physics, Bioinformatics, Control systems and engineering, Statistics
Domestic Tuition Fees (DTF)
For more information see: http://www.anu.edu.au/students/program-administration/costs-fees
- Annual indicative fee for international students
For further information on International Tuition Fees see: https://www.anu.edu.au/students/program-administration/fees-payments/international-tuition-fees
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.
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.
This program is available for applications to commence from First Semester, 2019
- 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.
- proficiently apply development tools for solving computer vision and machine learning problems.
- present the methodologies and implementation details in a concise and clear manner.
- conduct concept design, implementation, experimental analysis and testing consistent with current practice in computer vision and machine learning, including standard metrics and benchmark datasets.
- apply advanced knowledge, techniques and tools to real-world computer vision and machine learning applications.