Entry requirements
Admission criteria
To be eligible, an applicant must have achieved at least one of the following minimum entry requirements and demonstrate they fulfil any prerequisite and essential criteria for admission. In cases where there are more eligible applicants than available places, admission will be competitive with ranks based on the entry criteria.
- A completed bachelor (AQF level 7) or bachelor honours (AQF level 8) degree or equivalent from a recognised higher education institution; OR
- A completed nested or related graduate certificate (AQF level 8) or higher or equivalent from a recognised higher education institution.
This degree requires a Grade Point Average (GPA) equivalent to an Adelaide University 5.0 on a 7-point scale for entry.
English language entry requirements
In addition, international students who speak English as an additional language must have obtained one of the following standards within the last two years prior to admission. Possession of one or more of these qualifications, in addition to the academic entry requirements, does not, in itself, guarantee a place at Adelaide University. Applications are considered on an individual basis and selection is competitive. Where previous study/work experience was conducted in English, the application must be accompanied by certified documentation from the educational institution/employer certifying that the language of instruction/employment was English.
- IELTS Overall 6.5
- IELTS Reading 6
- IELTS Listening 6
- IELTS Speaking 6
- IELTS Writing 6
Please access the following link for a comprehensive list of English language tests accepted by Adelaide University and other important information in relation to meeting the University’s language requirements:
Equivalent English qualificationsDegree Structure: Master of Mathematical Sciences specialising in Artificial Intelligence
Program code: MAMTH
Complete 96 units comprising:
- 6 units for all Core courses, and
- 42 units for one Specialisation from Specialisation, and
- 24 units for all Work integrated learning, and
- 30 to 60 units for Electives
Study plan
A study plan sets out the courses you will need to complete and the recommended timing for each one. This will help to guide your enrolment in each study period, alongside any additional information provided below. If you have received a personalised plan or alternative advice from your Program Director, please refer to that guidance in the first instance, or seek help from your Program Director or Student Assist if you are unsure.
Year 1
Semester 1
Semester 2
Year 2
Semester 1
ARTI1000|Mathematical Generative Artificial Intelligence (6 units)
or
MATH6009|Deep Geometry (6 units)1 Course type: Program electives
or
MATH6012|Deep Signal Processing (6 units)1 Course type: Program electives
or
MATH6019|Reinforcement Learning (6 units)1 Course type: Program electives
ARTI1000|Mathematical Generative Artificial Intelligence (6 units)
or
MATH6009|Deep Geometry (6 units)1 Course type: Program electives
or
MATH6012|Deep Signal Processing (6 units)1 Course type: Program electives
or
MATH6019|Reinforcement Learning (6 units)1 Course type: Program electives
Semester 2
ARTI1000|Mathematical Generative Artificial Intelligence (6 units)
or
MATH6009|Deep Geometry (6 units)1 Course type: Program electives
or
MATH6012|Deep Signal Processing (6 units)1 Course type: Program electives
or
MATH6019|Reinforcement Learning (6 units)1 Course type: Program electives
ARTI1000|Mathematical Generative Artificial Intelligence (6 units)
or
MATH6009|Deep Geometry (6 units)1 Course type: Program electives
or
MATH6012|Deep Signal Processing (6 units)1 Course type: Program electives
or
MATH6019|Reinforcement Learning (6 units)1 Course type: Program electives
Enrolment information
Find details about the rules and notes that apply to your program, along with other essential information required for successful enrolment. Supplementary supporting resources may also be provided where applicable.
Some programs include additional enrolment advice. If available for your program, it will be displayed here.
Courses
Listed here are all the courses that contribute to your program, including elective options that can be chosen. These courses come together to form your study plan. Courses fall into different categories, each of which have specific unit values that need to be met under the program rules.
What courses you'll study
Complete 96 units comprising:
- 6 units for all Core courses, and
- 42 units for one Specialisation from Specialisation, and
- 24 units for all Work integrated learning, and
- 30 to 60 units for Electives
Complete 6 units for ALL of the following:
| Course name | Course code | Units | |
|---|---|---|---|
|
Course name
Multivariable Calculus
|
Course code
MATHX203
|
Units
6
|
|
Complete 42 units for ALL of the following:
| Course name | Course code | Units | |
|---|---|---|---|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Optimisation
|
Course code
MATHX205
|
Units
6
|
|
|
Course name
Probability
|
Course code
STATX200
|
Units
6
|
|
|
Course name
Stochastic Processes
|
Course code
STATX303
|
Units
6
|
|
|
Course name
Programming for Artificial Intelligence and Machine Learning 2
|
Course code
COMP5064
|
Units
6
|
|
|
Course name
Algebra
|
Course code
MATHX201
|
Units
6
|
|
Work Integrated Learning description
Complete 24 units for ALL of the following:
| Course name | Course code | Units | |
|---|---|---|---|
|
Course name
Mathematics Research Project 1
|
Course code
MATHX406
|
Units
12
|
|
|
Course name
Mathematics Research Project 2
|
Course code
MATHX407
|
Units
12
|
|
Electives description
Complete 30 to 60 units comprising:
- 30 to 60 units from Program electives
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Cryptography
|
Course code
MATH3022
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATH5018
|
Units
6
|
|
|
Course name
Deep Geometry
|
Course code
MATH6009
|
Units
6
|
|
|
Course name
Deep Signal Processing
|
Course code
MATH6012
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH6015
|
Units
6
|
|
|
Course name
Reinforcement Learning
|
Course code
MATH6019
|
Units
6
|
|
|
Course name
Complex Analysis
|
Course code
MATHX100
|
Units
6
|
|
|
Course name
Internship in Mathematics
|
Course code
MATHX101
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATHX103
|
Units
6
|
|
|
Course name
Differential Equations
|
Course code
MATHX202
|
Units
6
|
|
|
Course name
Numerical Methods
|
Course code
MATHX204
|
Units
6
|
|
|
Course name
Optimisation
|
Course code
MATHX205
|
Units
6
|
|
|
Course name
Real Analysis
|
Course code
MATHX206
|
Units
6
|
|
|
Course name
Advanced Differential Equations
|
Course code
MATHX300
|
Units
6
|
|
|
Course name
Advanced Mathematical Modelling
|
Course code
MATHX301
|
Units
6
|
|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Complex Networks
|
Course code
MATHX303
|
Units
6
|
|
|
Course name
Fluid Dynamics
|
Course code
MATHX304
|
Units
6
|
|
|
Course name
Functional Analysis
|
Course code
MATHX305
|
Units
6
|
|
|
Course name
Geometry of Surfaces
|
Course code
MATHX306
|
Units
6
|
|
|
Course name
Groups, Rings and Fields
|
Course code
MATHX307
|
Units
6
|
|
|
Course name
Measure and Integration
|
Course code
MATHX310
|
Units
6
|
|
|
Course name
Metric and Topological Spaces
|
Course code
MATHX311
|
Units
6
|
|
|
Course name
Number Theory
|
Course code
MATHX312
|
Units
6
|
|
|
Course name
Advanced Time Series
|
Course code
MATHX400
|
Units
6
|
|
|
Course name
Asymptotic Methods
|
Course code
MATHX401
|
Units
6
|
|
|
Course name
Category Theory
|
Course code
MATHX402
|
Units
6
|
|
|
Course name
Integral Transforms
|
Course code
MATHX403
|
Units
6
|
|
|
Course name
Lie Algebras and Lie Groups
|
Course code
MATHX404
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Riemannian Geometry
|
Course code
MATHX408
|
Units
6
|
|
|
Course name
Smooth Manifolds
|
Course code
MATHX409
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics A
|
Course code
MATHX410
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics B
|
Course code
MATHX411
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics A
|
Course code
MATHX412
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics B
|
Course code
MATHX413
|
Units
6
|
|
|
Course name
Natural Language Processing
|
Course code
MATHX415
|
Units
6
|
|
|
Course name
Probability
|
Course code
STATX200
|
Units
6
|
|
|
Course name
Stochastic Processes
|
Course code
STATX303
|
Units
6
|
|
|
Course name
Causal Inference
|
Course code
STATX403
|
Units
6
|
|
|
Course name
Advanced Computer Vision
|
Course code
COMP6032
|
Units
6
|
|
|
Course name
Programming for Artificial Intelligence and Machine Learning 2
|
Course code
COMP5064
|
Units
6
|
|
|
Course name
Information Theory
|
Course code
COMP4007
|
Units
6
|
|
|
Course name
Mathematical Generative Artificial Intelligence
|
Course code
ARTI1000
|
Units
6
|
|
|
Course name
Algebra
|
Course code
MATHX201
|
Units
6
|
|
|
Course name
Introduction to Topological Data Analysis
|
Course code
MATHX308
|
Units
6
|
|
|
Course name
Mathematical Biology
|
Course code
MATHX309
|
Units
6
|
|
|
Course name
Simulation for Decision Science
|
Course code
MATHX207
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH4010
|
Units
6
|
|
|
Course name
Advanced Stochastic Processes
|
Course code
MATHX200
|
Units
6
|
|
|
Course name
Statistical Theory
|
Course code
STAT2003
|
Units
6
|