Auckland University of Technology

What you'll study

This programme emphasises practical AI techniques, system design and deployment rather than purely theoretical research. Study is typically built from modular courses covering core foundations and applied topics, concluding with a hands-on project or capstone.

• Foundations of Machine Learning: supervised and unsupervised learning, model evaluation, feature engineering and statistical fundamentals.
• Deep Learning and Neural Networks: architectures for image, sequence and multimodal data; training, regularisation and transfer learning.
• Data Engineering for AI: data pipelines, preprocessing, databases, scalable data handling and introduction to cloud platforms.
• Natural Language Processing and Computer Vision: practical methods for text and image understanding, including embeddings, object detection and sequence models.
• AI Systems and Deployment: MLOps practices, containerisation, APIs, model monitoring and considerations for productionising AI applications.
• AI Ethics, Governance and Responsible Design: legal, social and ethical aspects of AI, algorithmic fairness, privacy and human-centred design.
• Project/Capstone: an applied project solving a real-world problem, often in partnership with industry or drawing on open datasets to develop and deploy a working AI solution.

Teaching methods combine lectures, lab work, practical assignments, group projects and a substantial applied project. Assessment typically mixes coursework, code-based assignments, reports and project deliverables.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree in computer science, information technology, engineering, mathematics, statistics or a closely related discipline. Applicants with qualifications in other fields may be considered if they can demonstrate substantial relevant computing experience or prior programming and quantitative skills.

• Demonstrable programming ability (for example in Python) and familiarity with basic data structures and algorithms.
• Prior exposure to statistics or probability is highly recommended.
• In some cases, applicants may be required to submit a portfolio of coding work or complete a diagnostic assessment to confirm readiness for graduate-level study.
• International applicants must meet the University’s English language requirements.
• Applicants without the formal prerequisites but with significant professional experience in AI, data science or software engineering may be considered on a case-by-case basis.

Career prospects

Graduates leave with practical skills that are directly applicable to industry roles. Typical career paths include:

• Machine Learning Engineer or AI Engineer — building and deploying models into production environments.
• Data Scientist — analysing data, designing predictive models and delivering insights to stakeholders.
• AI/ML Developer — integrating AI components into applications and services.
• Data Engineer — constructing and maintaining data pipelines and infrastructure that support AI workflows.
• AI Consultant or Technical Specialist — advising organisations on AI strategy, tools and implementation.
• Research and development roles in industry labs, or further study at postgraduate research level for those interested in academic careers.

Why study at Auckland University of Technology

AUT has a strong applied focus and close industry links, making it well-suited for students who want hands-on experience and workplace relevance. The School of Computer and Mathematical Sciences collaborates with local businesses, government and research groups, which helps provide project opportunities and exposure to real-world problems.

• Practical, project-led learning with access to modern computing labs and cloud resources.
• Emphasis on ethical and responsible AI, reflecting contemporary regulatory and societal concerns.
• Small-group teaching and industry-engaged projects that help build professional networks in Auckland and beyond.
• Support services for postgraduate students, including academic skills development, career guidance and connections to internships and placement opportunities.