MSc Artificial Intelligence

Overview

Durham University's MSc Artificial Intelligence develops the mathematical, algorithmic and systems competencies required of modern AI practitioners. You will progress from probability and classical learning through deep architectures, language models, vision and sequential decision-making, finishing with MLOps evaluation and governance. Lab work uses contemporary Python toolchains and structured peer review. The capstone produces a documented AI system with performance, safety and monitoring artefacts suitable for industrial or research scrutiny — not a demo notebook alone. MSc Artificial Intelligence is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 36. Students move from Mathematics & Probability for AI through Mathematics & Probability for AI; Machine Learning Theory & Practice; Programming Intelligent Systems (Python); Data Engineering for ML Pipelines; Deep Learning & Representation Learning; Natural Language Processing & Large Language Models, and finish on MSc Artificial Intelligence Capstone Project. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Design, train and evaluate supervised, unsupervised and deep learning models Build NLP and multimodal applications using modern transformer architectures Engineer reproducible ML pipelines with evaluation, monitoring and versioning Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

The curriculum aligns with the technical depth expected at leading research universities and industry AI labs, with explicit attention to the UK GDPR, EU AI Act literacy and model documentation practices (model cards, data sheets). Optional GPU compute is available for distinction-level projects. Pathway exists into the PhD Data Science for outstanding graduates.

Who This Programme Is For

• ✦Software engineers transitioning into ML engineering or applied research
• ✦Data scientists seeking formal AI systems and deep learning depth
• ✦STEM graduates building competitive credentials for AI product roles
• ✦Technical founders embedding AI responsibly into products

Programme Structure & Delivery

Learning Outcomes

• ✦Design, train and evaluate supervised, unsupervised and deep learning models
• ✦Build NLP and multimodal applications using modern transformer architectures
• ✦Engineer reproducible ML pipelines with evaluation, monitoring and versioning
• ✦Assess AI systems against fairness, safety and regulatory expectations
• ✦Communicate technical trade-offs to product and executive stakeholders

Career Pathways

• ✦Machine Learning Engineer
• ✦Applied AI Scientist
• ✦NLP / Computer Vision Engineer
• ✦MLOps Engineer
• ✦AI Product Technical Lead

Curriculum