Auckland University of Technology

What you'll study

This programme combines core technical foundations in machine learning and data processing with applied topics in areas such as computer vision, natural language processing, and AI systems engineering. Teaching emphasises hands-on implementation, model deployment, evaluation in production-like settings, and the social and ethical implications of AI.

• Core topics: supervised and unsupervised learning, deep learning architectures, probabilistic models, optimisation and statistical inference.
• Applied modules: computer vision, natural language processing, time-series and forecasting, recommender systems, and scalable data engineering for AI.
• Systems and deployment: cloud-based model deployment, MLOps practices, model monitoring and performance engineering.
• Professional and ethical practice: AI ethics, privacy, fairness and regulation, and the societal impacts of automation.
• Project work: a substantial applied project or research dissertation that typically involves industry-relevant problem scoping, dataset preparation, model development and evaluation, and delivery of a demonstrator or technical report.

Learning methods include lectures, practical laboratories, supervised projects and industry-linked case studies. Students gain experience with common AI tooling and frameworks, scalable data platforms, and best-practice software development for AI.

Entry requirements

• A recognised bachelor degree or equivalent in a relevant discipline such as computer science, engineering, mathematics, statistics, physics, information technology or a cognate field. Graduates with strong quantitative background from other areas may be considered.
• Applicants without a directly relevant degree but with substantial professional experience in software development, data analytics or related roles may be considered; such applicants may be required to demonstrate their technical preparation through prior coursework, a portfolio, or bridging study.
• Evidence of programming ability and familiarity with mathematics for machine learning is expected (for example, experience with Python or another high-level language, and with linear algebra, calculus and probability/statistics).
• All applicants must meet the university's English language proficiency requirements; international applicants typically provide a recognised English test score or documented evidence of prior tertiary study in English.
• Selection is competitive and may take into account academic record, professional experience, references and a statement of purpose outlining objectives and relevant skills.

Career prospects

Graduates are prepared for technical and applied roles where AI is a central component. Common career paths include:

• Machine Learning Engineer or AI Engineer
• Data Scientist or Applied Data Analyst
• Computer Vision or Natural Language Processing Specialist
• AI/ML Research Assistant or Developer in research-led teams
• MLOps Engineer, specialising in deploying and maintaining models in production
• Technical consultant or product specialist integrating AI into business solutions

Graduates can find work across sectors such as technology and software companies, finance, healthcare, telecommunications, government agencies and startups. The programme's project focus also provides a pathway into doctoral study for those interested in further research.

Why study at Auckland University of Technology

• Applied, industry-focused teaching: AUT emphasises practical skills and work-ready outcomes, with project components that connect students to real problems and industry partners.
• Technical resources: access to modern computing facilities, software tools and cloud platforms used in AI development, plus supervised lab work and group project environments.
• Strong regional links: located in New Zealand's largest city, AUT maintains connections with local tech firms, public sector organisations and startups, supporting internships, collaborative projects and employment opportunities.
• Experienced staff: teaching staff include academics and practitioners with backgrounds in machine learning, data science and software engineering, offering a balance of theoretical knowledge and practical guidance.
• Flexible pathways: the programme supports a range of students — from those seeking rapid reskilling to experienced professionals aiming to deepen technical expertise or move into leadership roles in AI delivery.