Graduate Diploma in Machine Learning New
Prosper through ‘thinking’ technology
Artificial intelligence (AI) and its component discipline machine learning now present the world’s single greatest commercial opportunity. Research indicates that by 2030 it could increase global GDP by up to 14%—a staggering US$15.7 trillion gain.
Employment prospects are equally outstanding. AI and machine learning appointments worldwide have doubled in the past three years, with demand easily outstripping supply.
The Graduate Diploma in Machine Learning, conducted through the University of Adelaide’s world-renowned Australian Institute of Machine Learning (AIML), will prepare you to thrive in this exciting future.
What will you do?
Our Graduate Diploma in Machine Learning is driven by AIML’s cutting-edge research and extensive industry links, spanning diverse international sectors – from health, medical technology, defence and security to environment and natural resources.
Selecting from either an Applied or Technical specialisation, you’ll undertake a carefully balanced mix of face-to-face intensive courses, work-integrated learning and online study. The 12-months full-time degree will equip you with:
- high-level technical skills in machine learning and AI application development, including in specialist areas, such as deep learning and visual question answering
- broad awareness of the commercial, organisational and research opportunities presented by machine learning and AI
- a deep understanding of the disciplines’ ethical and social considerations
- extensive industry connections and networks.
You’ll also receive ongoing mentoring, feedback and direction from AIML’s world-class researchers and high-performing industry professionals.
Where could it take you?
You could help companies or public service providers provide unheard levels of personalised service. You might play a part in fully automating cities’ public transport systems for unprecedented reliability. Perhaps you’ll have a role in reducing the risk of skin sun damage with personalised AI risk monitoring technology.
The degree also provides an outstanding foundation for further advanced study, offering significant credit towards our Master of Machine Learning.
Choose your applicant type to view the relevant admissions information for this program.
I am a:
SATAC Code 3GD105 Deferment Yes - 2 year Intake February and July Enquiries Future Students teamGraduate entry
Prerequisites SACE Stage 2 Mathematical Methods (or equivalent) Higher Education Study A completed Bachelor degree or equivalent with a minimum GPA of 4.5.SATAC Code: 3GD105
CRICOS 100823 Intake February and July
English Language Requirements
Australian Year 12 Successful completion of an Australian year 12 qualification with a minimum pass in an accepted English language subject English Tests accepted by the University of Adelaide IELTS Overall 6.5 Reading 6 Listening 6 Speaking 6 Writing 6 TOEFL Overall 79 Reading 13 Listening 13 Speaking 18 Writing 21 Pearsons Overall 58 Reading 50 Listening 50 Speaking 50 Writing 50 Cambridge Overall 176 Reading 169 Listening 169 Speaking 169 Writing 169 Qualifications that meet minimum English requirements A range of alternative qualifications may meet the University’s minimum English requirements
Academic Entry Requirements
Tertiary Qualifications A completed Bachelor degree or equivalent with a minimum GPA of 4.5
Australian Year 12 SACE Stage 2 Mathematical Methods (or equivalent) International Qualifications SACE Stage 2 Mathematical Methods (or equivalent)
Fees and Scholarships
Choose your applicant type to view the relevant fees and scholarships information for this program.
I am a:
Annual tuition feesAustralian Full-fee place: $36,000
Annual tuition fees International student place: $43,000
These scholarships, as well as many others funded by industry and non-profit organisations, are available to potential and currently enrolled students.
Computational Scientist, Computer Programmer, Computer Scientist, Diagnostic Technician, Digital Strategist, Software Specialist, IT Manager, IT Programmer
Degree StructureTo qualify for the degree of Graduate Diploma in Machine Learning, the student must complete satisfactorily a program of study consisting of the following courses with a combined total of not less than 24 units, comprising:
- Core courses to the value of 21 units
- Elective courses to the value of 3 units.
Example Study PlanStudy plans are available on the Faculty of Engineering, Computer and Mathematical Sciences website.
The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
Last updated: Friday, 27 Sep 2019