MSc Artificial Intelligence (online) (For students entering from May 2026 onwards)

Programme overview

Entry requirements

• Standard entry will require an honours degree equivalent to a UK first/upper second class, demonstrating aptitude for programming and quantitative reasoning in any mathematical / highly numerate undergraduate degree. Graduates with first degrees from most quantitative subject areas would be eligible, for example: mathematics, computer science, statistics, engineering (any), physics / physical sciences, space science, econometrics, quantitative research methods etc.
• Graduates who hold an honours degree equivalent to a UK lower second class from a mathematics based discipline (see examples above) may also be eligible providing they can demonstrate relevant professional experience, a minimum of 3 years in related professional environment.
• Graduates who hold an honours degree equivalent to a UK first/upper second class from non-mathematics-based disciplines may also be eligible providing they can demonstrate relevant professional experience, a minimum of 3 years in a related professional environment.
• For students whose first language is not English, an English language qualification at a suitable level: IELTS 6.5 or equivalent with no lower than 6.5 in each category.

Programme specification

This online programme provides a rigorous and contemporary education in the principles, methods, and practice of modern artificial intelligence. It develops the knowledge, skills, and critical perspective required to design, implement, and evaluate intelligent systems that learn from data, adapt through experience, and interact effectively with their environment.

Students begin by building strong foundations in programming, mathematics, and ethical reasoning, gaining the analytical fluency and computational confidence required to engage deeply with AI technologies. These foundations support progression into classical and modern machine learning, where students study how models represent structure, uncertainty, and pattern in data. The curriculum advances toward deep learning, exploring how neural architectures underpin current progress in vision, language, and generative modelling, before extending to reinforcement learning and agentic AI that enable adaptive, goal-directed behaviour. It is expected that students will complete all modules within each carousel before progressing to the next carousel.

A distinctive feature of the programme is its focus on AI as an integrated design discipline that links theory, computation, and responsible practice. Students learn to operationalise models through MLOps workflows, bridge multiple data modalities, and critically evaluate issues of alignment, fairness, and accountability in real-world deployment.

Graduates emerge with the conceptual understanding, technical capability, and reflective awareness required to contribute to the next generation of AI research and applications across domains such as engineering, health, finance, and the sciences, or to continue toward doctoral study in artificial intelligence and machine learning.

Year 1

(online) (For students entering from May 2026 onwards)

[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable

Compulsory Modules

Candidates will study the following compulsory modules

Code	Title	Credits	Semester	Pass for Progression
OCOM5100M	Programming for Data Science	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OCOM5104M	Ethics of Artificial Intelligence	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OCOM5105M	Mathematical Foundations of Artificial Intelligence	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug
OCOM5206M	Machine Learning	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OCOM5207M	Neural Networks & Deep Learning	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug
OCOM5208M	Machine Learning Operations	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)

Year 2

(online) (For students entering from May 2026 onwards)

[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable

Compulsory Modules

Candidates will study the following compulsory modules

Code	Title	Credits	Semester	Pass for Progression
OCOM5250M	Deep Learning for Computer Vision	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OCOM5251M	Deep Learning for Natural Language Processing	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OCOM5252M	Reinforcement Learning and Modern learning Paradigms	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug
OCOM5300M	Artificial Intelligence Project	45	1 Jan to 30 Jun, 1 Jul to 31 Dec, 1 Mar to 31 Aug, 1 Mar to 31 Aug (6mth)(adv yr), 1 May to 31 Oct, 1 May to 31 Oct (6mth)(adv yr), 1 Nov to 30 Apr, 1 Nov to 30 Apr (6mth)(adv yr), 1 Sep to 28 Feb, 1 Sep to 28 Feb (6mth)(adv yr)	PFP

Last updated: 15/05/2026 11:23:51

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