University of Technology Sydney

What you'll study

The curriculum builds a foundation in programming, algorithms, discrete mathematics and statistics before moving into specialised AI topics. Typical subjects include programming for AI (often Python), data structures and algorithms, probability and statistics, linear algebra and optimisation, machine learning, deep learning, natural language processing, computer vision, and robotics. The degree emphasises practical skills: database and data engineering, software development for AI, cloud platforms and deployment, and model evaluation and interpretability.

Students also study the social, ethical and legal implications of AI, including fairness, privacy, transparency and human-centred design. Learning is delivered through a mix of lectures, laboratories, tutorials and project-based units; a major industry-linked capstone project or practicum is a common feature, enabling students to apply methods to real-world datasets and problems. Elective streams and interdisciplinary options allow students to combine AI with disciplines such as business, design, health or creative technologies, and there are clear pathways to honours or postgraduate study for research-minded graduates.

Entry requirements

Admission is based on successful completion of secondary school qualifications or recognised equivalent tertiary preparation. Competitive applicants typically have a strong background in mathematics; specific prerequisites usually expect a higher-level secondary mathematics subject or demonstrated competence in mathematical methods. Applicants must meet the University's English language requirements.

UTS recognises a range of admission pathways: selection by academic achievement (via centralised admission systems), alternative admission schemes, recognised prior learning and bridging or preparation courses. International applicants should present equivalent qualifications from their home country and satisfy any additional documentation requirements. Meeting the minimum requirements does not guarantee offer — selection may be competitive and include consideration of the overall academic profile.

Career prospects

Graduates work across technology and non-technology sectors where data-driven decision making and automation are important. Typical roles include:

• Machine learning engineer – designing and implementing predictive models and production pipelines.
• Data scientist / data analyst – extracting insights from data, performing statistical analysis and visualisation.
• AI software developer – integrating AI components into applications and services.
• Computer vision or NLP specialist – developing systems for image, video or language understanding.
• Robotics and automation engineer – applying AI to control and autonomous systems.
• AI policy, ethics or product roles – advising on responsible AI deployment, governance, and product management.

Graduates find employment in sectors such as technology firms, finance, healthcare, government, consultancy, startups and research organisations. The degree also provides a foundation for further study at honours, master's or PhD level for careers in research and advanced technical leadership.

Why study at University of Technology Sydney

UTS offers a practice-focused approach to AI education with strong links to industry in Sydney’s technology and innovation ecosystem. The university provides hands-on learning through labs, project work and industry practicums, and students often benefit from collaborations with industry partners and opportunities to work on real projects. UTS also has active research groups and centres in data science and artificial intelligence, supporting a pathway to research and postgraduate study.

Studying at UTS gives access to modern computing facilities, opportunities for interdisciplinary collaboration, and career support services that help students transition into professional roles. The program’s emphasis on ethical, human-centred AI prepares graduates to develop responsible and socially aware AI solutions in diverse workplaces.