| UCAS code |
1234 |
| Duration |
1 year full time |
| Entry year |
2026 |
| Campus |
Streatham Campus
|
Why study MSc Advanced Machine Learning at Exeter?
- Develop deep expertise in machine learning theory and practice, from foundational algorithms to advanced deep learning, combining rigorous mathematical foundations with hands-on application.
- Work with real-world data challenges using industry-standard tools, building the technical and analytical skills expected of machine learning specialists.
- Benefit from Exeter’s connection to the Alan Turing Institute through the Turing University Network and access a wider research community, including Turing Fellows based at Exeter.
- Learn within a research-led environment where teaching is delivered by active researchers in AI, machine learning and deep learning, ensuring your studies reflect the latest developments.
- Study flexibly full-time over one year or part-time over two years, and graduate prepared for careers in AI engineering, data science, research and the wider technology sector.
Top 20 in the UK for Computer Science
Advanced cloud computing infrastructure supporting complex AI model development
Teaching draws upon our research strengths in artificial intelligence and high-performance computing
Entry requirements
We welcome applications from graduates with a 2:1 honours degree or above in computer science or a closely related discipline.
We consider each application on its individual merits, and we encourage you to apply if you have relevant professional experience or a strong technical background that falls outside these standard criteria. If English is not your first language, you will need to meet our standard English language requirements. Full details are available on our international students pages.
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
The MSc Advanced Machine Learning is structured to build your expertise progressively across the academic year, combining rigorous mathematical foundations with hands-on application to develop both theoretical understanding and practical skill. In the taught phase, you will engage with compulsory modules that establish core knowledge in machine learning, statistical learning theory and deep learning, while working with real-world data using industry-standard tools. Alongside this, optional modules allow you to specialise in areas aligned with your interests and career ambitions.
The programme is delivered within a research-led environment, where teaching is informed by active work in AI, machine learning and data science. As part of the University’s connection to the Alan Turing Institute through the Turing University Network, you will be part of a wider national research community, with links to Turing Fellows based at Exeter.
The programme concludes with an independent research dissertation, in which you will identify a significant problem, design a solution and communicate your findings to a professional standard. This project offers the opportunity to work closely with an academic supervisor and produce original work in a rapidly evolving field.
The modules below provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
Please note that the module information displayed here is subject to change.
Compulsory modules
| Code | Module | Credits |
|---|
| 105 credits of compulsory modules: |
| COMM113 | Deep Learning | 15 |
| COMM114 | Generative AI | 15 |
| COMM120 | Reinforcement Learning | 15 |
| COMM514 | Research Project | 60 |
Optional modules
| Code | Module | Credits |
|---|
| 75 credits of optional modules: |
| COMM112 | Design Methods for Human-Centred AI | 15 |
| ECMM447 | Social Networks and Text Analysis | 15 |
| COMM042 | Introduction to Computer Vision | 15 |
| ECMM422 | Machine Learning | 15 |
| COMM119 | AI in Environment | 15 |
| COMM118 | AI in Healthcare | 15 |
| ECMM426 | Computer Vision | 15 |
| COMM040 | Text Mining and Natural Language Processing | 15 |
| COMM117 | Large Language Models and Applications | 15 |
| COMM116 | Generative AI Applications | 15 |
| ECMM461 | High Performance Computing | 15 |
| COMM115 | Data Science at Scale | 15 |
Fees
2026/27 entry
UK fees per year:
£12,900 full-time
International fees per year:
£29,800 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
Alan Turing Institute partnership
The University of Exeter is a member of the Alan Turing Institute University Network. The Turing was established in 2015 as the UK's national institute for data science and artificial intelligence. Exeter hosts six Turing Fellows, and the University's Institute for Data Science and Artificial Intelligence (IDSAI) is closely connected to this national network. Studying at Exeter places you within a community shaped by leading researchers who contribute to some of the most pressing questions in AI and data science today. Find out more about our Turing Institute partnership.
Teaching and assessment
The programme is delivered through a mix of lectures, seminars, tutorials, industrial presentations, case studies, industry visits, computer simulations, project work and a dissertation.
You will develop transferable skills such as management and communication skills, computational techniques, data handling and analysis, problem solving, decision making and research methodology. Many of these will be addressed within an industrial and commercial context.
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.
Maths support
Throughout your studies you will have access to support materials designed to refresh your knowledge of engineering mathematics. Mathematics for Engineers is a non-credit bearing online resource that covers the mathematics needed to undertake an MSc. It is an invaluable revision tool for those wishing to refresh their knowledge of common mathematical topics.
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Professor Richard Everson
Professor Shiqiang Wang
Professor Edward Keedwell
Dr Zeyu Fu
Professor Richard Everson
Professor Richard Everson graduated in Physics from the University of Cambridge and completed a PhD in Applied Mathematics at the University of Leeds. Before joining the University of Exeter in 1999, he worked at Brown, Yale, Rockefeller University and Imperial College London on projects spanning fluid mechanics, optical imaging and data analysis. His research focuses on machine learning, statistical pattern recognition and optimisation, with current applications including wireless networks, healthcare technologies, animal behaviour analysis and big data systems.
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Professor Shiqiang Wang
Professor Shiqiang Wang is Professor of Artificial Intelligence in the Department of Computer Science at the University of Exeter. Previously a researcher at IBM T. J. Watson Research Center in New York, he completed his PhD at Imperial College London in 2015. His research focuses on artificial intelligence, distributed computing and optimisation, with applications including large language models, federated learning and AI systems. He has received multiple international awards for his work and serves in editorial and leadership roles across major AI and machine learning conferences and journals.
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Professor Edward Keedwell
Professor Ellis Keedwell is Professor of Artificial Intelligence at the University of Exeter. His research focuses on optimisation, machine learning and AI-based simulation, with applications in engineering and bioinformatics. He leads a research group in applied artificial intelligence and has contributed to funding awards totalling more than £3.75 million from organisations including EPSRC, Innovate UK and industry partners. His current work includes AI for transport systems, telecommunications scheduling, quantum computing applications and human-centred optimisation methods.
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Dr Zeyu Fu
Dr Zeyu Fu is a Lecturer in Computer Vision and Machine Learning at the University of Exeter, where he leads the Multimodal Intelligence Lab. His research explores how multimodal AI, computer vision and machine learning can support a greener, healthier and fairer society, with applications in healthcare, environmental science and social research. Prior to joining Exeter, he held research roles at the University of Oxford and Newcastle University, working on projects involving biomedical imaging, ultrasound AI systems and human tracking using machine learning and signal processing.
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The Babbage and Lovelace computer labs are comfortable, pleasant and engaging environments that are sensory-friendly - they are quieter and less cluttered than traditional computer labs.
The desks in the Lovelace and Babbage computer labs are arranged in banks of six with a display monitor on each bank.
The display monitor on each bank can either show teaching material or share group work from each area.
Monitors on each desk fold down to encourage engagement and build group work skills when screens are not in use.
The Lovelace computer laboratory seats 120 students.
The Babbage computer laboratory seats 60 students.
There is a breakout space next door to the Babbage computer lab which can be used for seminars and discussion groups. The space has wraparound whiteboards and is set up to encourage collaborative working.
The nearby social space enables you to socialise or get refreshments before or after your labs.
Careers
Graduates of the MSc Advanced Machine Learning will be equipped to take on advanced technical roles at the forefront of AI innovation, with the skills to develop and apply next-generation machine learning techniques across a range of sectors.
Building on a strong foundation in computer science, the programme prepares you for specialist positions such as Machine Learning Engineer, AI Research Scientist, Data Scientist (Machine Learning), or roles in areas such as computer vision, natural language processing and reinforcement learning. Its emphasis on first-principles understanding, real-world application and cutting-edge research enables graduates to contribute effectively within commercial AI organisations, technology companies and data-driven industries.
Through engagement with leading academics, access to advanced facilities and connections to the Alan Turing Institute, you will also be well-positioned to progress to doctoral study or research-led careers, with the adaptability to evolve alongside this fast-moving field.
Employer-valued skills
This programme develops a combination of advanced technical and transferable skills sought by employers across the technology, finance, healthcare and public sectors. You will gain expertise in machine learning, statistical modelling and data-driven problem-solving, alongside experience in independent research and working with complex real-world data. In parallel, you will strengthen your critical thinking, analytical reasoning and ability to communicate technical ideas clearly to both specialist and non-specialist audiences.
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.
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