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Artificial intelligence is increasingly used in high-stakes settings, from healthcare and finance to government and technology. In these environments, employers need people who can do more than build models. They need people who can evaluate outputs carefully, understand uncertainty, and judge when AI systems are reliable enough to support real decisions.This MSc is a statistics-first route into AI. It provides the statistical foundations that make AI work and make it trustworthy. You will study probability, statistical inference, Bayesian modelling, machine learning and optimisation, with dedicated study of deep learning. You will also learn how to assess AI outputs as statistical evidence, with attention to uncertainty, assumptions and limitations.The programme is taught hands-on in Python and R. You will develop practical fluency in reproducible computational workflows for statistical and machine learning analysis, together with a critical understanding of calibration, robustness, distribution shift, subgroup effects, fairness, data provenance and governance in AI-related settings. This prepares you for roles in applied machine learning, model validation, AI teams and other areas where statistical judgement matters.A major feature of the programme is its consultancy-style dissertation format, based on a proven model already used successfully across related programmes in the School's Statistics MSc portfolio. In the summer, you will complete two case projects in different application areas, giving you the chance to apply statistical and AI methods to realistic briefs involving machine learning, uncertainty, model evaluation and decision-ready evidence, build breadth across more than one domain, and develop the communication skills needed in professional practice.Edinburgh offers a distinctive environment for this training through the School of Mathematics, with close links to internationally recognised AI research and teaching elsewhere in the University, including Informatics, and to the wider data-driven innovation ecosystem through the Bayes Centre.
