University of Adelaide

What you'll study

This programme builds core mathematical skills alongside specialised AI and machine learning topics. Study typically combines coursework in advanced mathematics, statistics and computational methods with targeted AI units and a substantial project or research component.

• Core mathematical foundations — measure-theoretic probability, advanced linear algebra, optimisation, and statistical inference that underpin modern AI methods.
• Machine learning and AI — supervised and unsupervised learning, deep learning architectures, probabilistic graphical models, Bayesian methods and ensemble techniques.
• Computational methods — numerical algorithms, high-dimensional data techniques, stochastic optimisation, and scalable computing for large datasets.
• Applied statistics and data analysis — time series, multivariate analysis, experimental design and methods for uncertainty quantification.
• Ethics, interpretability and safety — responsible AI, model interpretability, fairness and human-centred considerations in deployment.
• Capstone project or research thesis — an individual or group research project applying mathematical and AI methods to a real problem, often undertaken in collaboration with academic supervisors or external partners.

Students can expect a mixture of lectures, practical labs, programming assignments (commonly using Python and relevant libraries), and a supervised research or industry-linked project that demonstrates applied skills.

Entry requirements

Applicants should hold an undergraduate degree with significant mathematics, statistics or computer science content. Typical applicants have a bachelor’s degree with substantial quantitative coursework or an honours degree in a related discipline.

• Academic background in calculus, linear algebra, probability and statistics, and some programming experience is normally required.
• Recent or relevant professional experience in quantitative roles can strengthen an application where academic background is less conventional.
• Applicants whose first language is not English will need to demonstrate English proficiency by providing an accepted test score or equivalent evidence, in line with University of Adelaide admission policies.
• Standard application materials include academic transcripts, a curriculum vitae, a statement of purpose describing research or career goals, and referees. Some applicants may be asked to supply examples of prior coursework or coding experience.

Career prospects

Graduates leave equipped for roles that require strong mathematical modelling and machine learning expertise. Common career paths include:

• Machine learning engineer or AI developer building production systems.
• Data scientist or analyst working across industry sectors such as finance, health, defence, and retail.
• Quantitative analyst or modeller in risk, energy, or trading firms.
• Research scientist or research assistant in university or industry research labs, or progression to doctoral study.
• Roles in government and public sector agencies focused on policy modelling, forecasting and data-driven decision-making.

The programme emphasises transferable skills — mathematical rigour, algorithm design, software implementation and communication of technical results — valued across technology, science and business environments.

Why study at University of Adelaide

The University of Adelaide offers this programme within a strong School of Mathematical Sciences and has active research groups in applied mathematics, statistics and data science. Students benefit from access to experienced academic supervisors, collaboration opportunities with the University’s interdisciplinary institutes, and links to industry partners and research centres across South Australia.

Facilities include computing resources suitable for machine learning and data-intensive work, and students can engage with seminars, workshops and internship possibilities that connect academic learning to practical problems. The university’s campus and Adelaide’s growing technology and research community provide a supportive environment for study and professional development.