MSc Artificial Intelligence
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | 2:2 degree in a non-related science undergraduate degree, or a 2:2 in any other degree subject and A Level Mathematics at Grade A or equivalent. |
|---|---|
Why study MSc Artificial Intelligence at Exeter?
- Start your AI journey with no coding background – we’ll guide you every step of the way.
- Be inspired by world-leading experts shaping the future of AI and data science.
- Unlock access to cutting-edge labs and innovation spaces powered by major ADA investment.
- Work on real-world projects that showcase your skills to future employers.
- Graduate with the expertise to thrive in high-impact AI roles across global industries.
Top 20 in the UK for Computer Science
Partner to the Alan Turing Institute
Advanced cloud computing infrastructure supporting complex AI model development
Teaching draws upon our research strengths in artificial intelligence and high-performance computing
Entry requirements
Applicants are required to have at least a 2:2 degree in a non-related science undergraduate degree, or a 2:2 in any other degree subject and A Level Mathematics at Grade A or equivalent.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course.
The required IELTS test scores for this course fall under Profile B3.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Course content
Our MSc Artificial Intelligence is designed for graduates from any discipline who want to pivot into one of the world’s fastest-growing fields. No prior coding experience is required - we’ll take you from programming fundamentals to advanced AI concepts, equipping you with the technical expertise and critical insight to apply AI responsibly.
You’ll learn from world-leading researchers at our Institute for Data Science and Artificial Intelligence, gaining access to cutting-edge labs and facilities. The programme blends theory with practice, giving you the opportunity to work on real-world projects, prototype AI solutions, and explore the social and ethical dimensions of AI.
By the time you graduate, you’ll be prepared to step into high-demand roles such as Machine Learning Engineer, AI Product Manager, or Data Scientist, with the skills to make a meaningful impact across industries worldwide.
The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. Because AI is such a rapidly changing field, the precise modules available to you in future years will vary depending to accommodate cutting-edge research and techniques, staff availability, timetabling and student demand.
Please note that the module information displayed here is subject to change.
150 credits of compulsory modules, 30 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Applications of AI | 15 | |
| Programming with Python | 15 | |
| Foundations of Human-Centred AI | 15 | |
| Research Project | 60 | |
| Machine Learning | 15 | |
| Introduction to Data Science | 15 | |
| Learning from Data | 15 | |
COMM109: Programming with Python
This module will introduce students to the fundamentals of constructing software using the Python programming language. You will learn how to decompose problems into components that can be implemented to provide a software solution, as well as how to control program flow and represent data within software. Having learned the fundamentals of Python coding you will be introduced to exception handling, Python classes, and be introduced to principles of software development and testing.
COMM111: Foundations of Human-Centred AI
You will study foundational concepts in how to design Artificial Intelligence (AI) systems that interact with humans. This will involve learning about human psychology including computational theories of how people represent and process knowledge, learn and work together. You will learn about topics including, how people make decisions, how they perform perceptual/manual tasks, how human vision works. You will use these theories to build and critically evaluate Artificial Intelligence systems that work with people.
You should take this module if you are interested in going on to a masters/research degree and/or in the rapidly expanding number of career pathways that involve designing AI to work with people. For these careers learning about human psychology is vital to designing systems that, for example, people find useful but not controlling and people find engaging but not addictive. For example, answers to the following questions require an understanding of the psychology of the user. How can AI be fine-tuned to human preferences and emotions? How can an AI system learn about an individual person’s goals and preferences? How can it learn about their emotions and feelings about others? Answers to these questions can help improve AI systems that work with people in the workplace and the home.
COMM514: Research Project
In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.
Research topics can be selected from across the breadth of computer science, data science and related topics. The project may include theoretical analysis, as well as practical software implementation.
This module aims to give you in-depth experience of research in an area relating to your programme of study. It will help you prepare for projects both in an industry or commercial setting, as well as in further postgraduate research work, such as a PhD. The module builds on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis and presentation of results.
ECMM422: Machine Learning
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.
In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.
ECMM443: Introduction to Data Science
In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, statistical modelling, network and text data analysis. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises and individual study will consolidate your learning and provide the foundations for later study.
This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing students with the tools to formulate data science problems and construct pipelines to begin to solve them technically.
Lectures will be accompanied by data analysis exercises. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation.
ECMM445: Learning from Data
Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.
The module will also cover some recent neural network architectures and related learning algorithms.
This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Introduction to Computer Vision | 15 | |
| Design Methods for Human-Centred AI | 15 | |
| Social Networks and Text Analysis | 15 | |
| Data Governance and Ethics | 15 | |
COMM042: Introduction to Computer Vision
How do we recognise objects and people? How can we catch a ball? How do we navigate our way from our desk to the coffee machine, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in computer vision have seen significant improvement in important applications (face detection in cameras, body tracking, and autonomous cars).
This module will provide you with the fundamentals of computer vision, covering the essential challenges and key algorithms for solving a variety of vision problems. The course will provide both theoretical grounding in the relevant theories and a blend of classical and state-of-the-art approaches to computer vision problems. The course will focus on practical applications of computer vision and cover a broad range of problems, from low-level image processing to object recognition, tracking and 3D vision.
COMM112: Design Methods for Human-Centred AI
Learn the skills needed to practice Human-centred design of Artificially Intelligent systems. You will learn how to use computational design thinking to empathise with people, ideate, prototype and evaluate AI systems. You will learn how to abstract AI problems by engaging with people, communities and contexts. You will apply methods from Human-Computer Interaction to engage with users through participatory design practices.
Having used these methods to abstract Human Centred AI problems, you will learn how to investigate prototype solutions and critically analyse their strengths and weaknesses, both from a computational perspective and a human perspective.
You will attend a weekly class in which an expert in Human-centred AI will lead discussion of an aspect of Human Centred AI design and its implications for how relevant Artificial Intelligence technologies are likely to impact people.
This course is a hands-on, practice-oriented approach to learning Human-centred AI (HCAI) design, with a strong emphasis on the evaluation of AI systems from both technical and human perspectives. Methods covered will include design thinking, participatory design, A/B testing, think-aloud protocols, diary studies, eye tracking studies etc. These tools provide students with the skills required to work with people to understand their needs and desires, understand how and why they perform tasks as they do and design AI systems that work with and for them.
ECMM447: Social Networks and Text Analysis
The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.
The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open-source packages.
The module will be taught in weekly lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material.
SOCM033: Data Governance and Ethics
Data science, machine learning, artificial intelligence and 'big data' have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.
Fees
2026/27 entry
UK fees per year:
£14,300 full-time
International fees per year:
£30,300 full-time
Scholarships
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships. We also provide awards for sport, music and other achievements, as well as regional and partner scholarships with organisations such as Chevening, The Beacon Trust and the British Council. For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
University of Exeter Alumni Scholarship
We are pleased to offer the University of Exeter Alumni Scholarship, a scholarship for University of Exeter alumni beginning a standalone postgraduate programme in 2026/27 with us a scholarship worth 20% of the cost of your first year tuition fees.
Terms and conditions, including deadlines, apply.
Teaching and research
Teaching and assessment
You’ll learn through a mix of interactive lectures, hands-on projects, and guest sessions from industry experts, exploring both the technical and human sides of AI. You’ll be assessed through coursework, group projects, and a final independent dissertation, giving you the chance to showcase your skills and apply them to real-world challenges.
Personal Tutor
You will be allocated a Personal Tutor who is available for advice and support throughout your studies, along with support and mentoring from graduates who are now in industry. There is also a Postgraduate Tutor available to help with further guidance and advice.
A research and practice-led culture
We believe every student benefits from being taught by experts active in research and practice. You will discuss the very latest ideas, research discoveries and new technologies in seminars and in the field. Plus, you’ll become actively involved in a research project yourself.
All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will also be taught by leading industry practitioners.
Dr David Walker is Senior Lecturer in Computer Science at the University of Exeter. He specialises in evolutionary computation, visualisation, data science, AI and optimisation. He teaches modules in AI, software engineering, machine learning, databases and optimisation.
His research focuses on developing methods that make AI and optimisation processes more transparent and interactive through visualisation, improving both algorithm design and interpretation.
Read more from Dr David Walker
Dr David Walker
Programme Director for MSc Artificial Intelligence
Careers
Artificial intelligence is transforming industries worldwide, creating huge demand for professionals with the skills to design, deploy, and manage intelligent systems. As a graduate of this programme, you’ll be equipped with technical expertise in programming, machine learning, and data science, as well as the critical insight to consider AI’s ethical and social impact. This combination makes you highly sought after in sectors such as healthcare, finance, logistics, robotics, and technology, where AI-driven solutions are shaping the future.
Graduate Destinations
Our MSc Artificial Intelligence graduates are well prepared for diverse and exciting career paths. Popular roles include Machine Learning Engineer, Data Scientist, AI Product Manager, Research Scientist, and AI Ethics Consultant. Many of our students also go on to pursue further study at doctoral level or move into research and development positions within leading technology companies. With demand for AI expertise continuing to rise, your skills will be valued across global markets.
Dedicated careers support
You will receive support from our dedicated Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in developing your enterprise acumen. Please visit the Career Zone for additional information on their services.
