MSc Generative 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 | 2:1 in Computer Science or a related area. |
|---|---|
Why study MSc Generative Artificial Intelligence at Exeter?
- Delve into the transformative world of AI systems that generate content and create innovative solutions
- Explore core theoretical principles and methodologies of generative models, focusing on their diverse applications, from natural language processing, text-to-image synthesis, and creative industries
- Discover the intersections of generative AI and computer science, linguistics, healthcare, environmental science and more
- Based on a foundation of leading research into the ongoing development and integration of generative AI and its impact on the future of work and business
Top 20 for Computer Science
20th in The Times and The Sunday Times Good University Guide 2024
Top 10 in the UK for graduate prospects
Joint 9th for graduate prospects for Computer Science in the Complete University Guide 2027
Long-established partnership with the Alan Turing Institute and home to the Institute of Data Science and Artificial Intelligence
"Before joining Exeter, I came from an engineering background…When I came across the MSc Generative AI course at Exeter, it stood out to me because there are still relatively few courses focused specifically on generative AI. Exeter’s decision to create a dedicated programme in this field was one of the main reasons I chose the university."
"…Modules were also taught in a way that I found especially useful, such as through research paper discussions. These sessions helped me better understand the current research landscape and improved my ability to approach and analyse academic papers. I also found the lecturers to be very approachable and enjoyable to speak with, which made it easier to ask questions, discuss ideas, and engage more deeply with the course content.
I also appreciated the practical nature of the coursework, with projects such as building agents, as well as the research project, where I am currently developing a new efficient solver for diffusion models, which are commonly used for image generation.
In terms of my future career plans, I intend to continue working within the AI space. I have secured a role at Deloitte as a Senior Consultant, where I will be working on a range of projects that will allow me to apply both my AI knowledge and engineering background."
George
MSc Generative Artificial Intelligence
Entry requirements
2:1 in Computer Science or a related area.
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 B1.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Course content
By joining our cutting-edge Masters in Generative Artificial Intelligence, you will delve into the transformative world of AI that focuses on generating content and creating innovative solutions. This programme will equip you with in-depth knowledge of the principles and technologies underpinning generative AI, from foundational machine learning concepts and data-driven modelling to deep learning and advanced techniques such as large language models. A key component is the ethical and societal implications of generative AI. The programme will foster the development of responsible AI systems that can contribute positively to society, preparing you to engage critically with the challenges and opportunities of this rapidly evolving field.
You will explore the core methodologies of generative models and their diverse applications in fields like natural language processing, text-to-image synthesis, and creative industries. The programme balances theoretical knowledge with practical skills, providing a comprehensive understanding of generative AI.
This programme promotes collaboration across diverse domains, encouraging you to explore the intersections of generative AI with computer science, linguistics, healthcare, environmental science and more. It aims to broaden your perspective and spark innovative applications of generative AI.
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.
120 credits of compulsory modules, 60 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Deep Learning | 15 | |
| Generative AI | 15 | |
| Data Science at Scale | 15 | |
| Generative AI Applications | 15 | |
| Research Project | 60 | |
COMM113: Deep Learning
Deep Learning is a highly in-demand skill in AI. In this module, you will study foundational and advanced deep learning techniques, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn key concepts, including, for example, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and contemporary advancements such as Transformers, with practical applications across various domains. You will attend lectures providing in-depth coverage of theories and algorithms. In addition, you will attend lab sessions where you'll apply theoretical concepts to hands-on practices. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and foundational machine learning concepts.
COMM114: Generative AI
Generative models are widely used in many subfields of AI, making Generative AI a rapidly evolving and transformative field. In this module, you will study the theoretical foundations, for example, the probabilistic foundations and learning algorithms for generative models, variational autoencoder (VAE), generative adversarial networks (GANs), flow matching, and diffusion models. You will also study application areas that have benefitted from generative models. You will attend lecture sessions, complemented by lab sessions, allowing you to apply your knowledge through hands-on exercises. You will work with popular AI frameworks and generative AI libraries, gaining practical experience. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and deep learning.
COMM115: Data Science at Scale
Data science and some machine learning technologies rely on large amounts of data to be effective and many commercial and scientific applications require the analysis of large quantities of heterogenous, noisy data on distributed machines. This module will examine the ways in which algorithms for data science can be implemented for large data and will discuss new algorithms specifically designed for large scale data. You will also work with large-scale distributed and cloud systems for storing and computing with big data.
Through theory and practice this module aims to equip you with an understanding of the principles of distributed computing, particularly on cloud-based systems, the ways in which data can be stored and accessed to allow efficient computation, and efficient algorithms for large-scale computation.
Distributed cloud computing will provide you with the underpinning knowledge required to develop and implement machine learning and artificial intelligence algorithms on distributed high-performance computing systems.
COMM116: Generative AI Applications
Generative AI is driving innovative applications in different sectors. In this module, you will familiarise yourself with the foundations and essential tools of generative AI and focus on its key applications across various domains and their potential impact on industries and society. You will design and implement generative AI techniques for real-world applications in domains such as healthcare, environment, art, and entertainment. You will attend lecture and lab sessions, where you will learn to analyse and fine-tune generative models to optimise their performance for your chosen application(s). This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and machine learning.
Pre-requisite modules: COMM113 Deep Learning
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.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Network Science | 15 | |
| Text Mining and Natural Language Processing | 15 | |
| Large Language Models and Applications | 15 | |
| Machine Learning | 15 | |
| Computer Vision | 15 | |
| Data Governance and Ethics | 15 | |
COMM039: Network Science
Many of the most important datasets are relational: friends and followers on social media, users who buy similar products, towns connected by roads, computers connected by routers and so on. Network Science is how we study and understand large relational data sets. In this module you will study how to represent, visualise, summarize and analyse large networks to learn about communities, epidemics, transport and resilience in real systems. This module is appropriate for students interested in data science and requires some programming and math background.
This module aims to give students the expertise to model and analyse large network datasets. After a grounding in the basics of network science we move on to more advanced techniques and algorithms to perform rigorous analysis of large networks and showing how these methods can be applied to real data sets.
COMM040: Text Mining and Natural Language Processing
Text mining is the process of extracting insight from large collections of written documents. Recently, there has been immense progress in how computers understand human language. This means reviews, tweets, archives of legal documents, recipes and all kinds of text can now be effectively analysed. This module teaches you how to search, group, summarise and understand large corpuses of documents. The course will cover methods like topic modelling, sentiment analysis, translation and the use of Large Language Models to solve real world problems. The student should have taken or be taking a module on the basics of machine learning.
Students will understand and apply modern NLP methods to real world textual datasets. The focus will be on methods for generating insight from large collections of text, from practical first steps, like data cleaning and validation, to topic modelling using a variety of cutting-edge techniques.
COMM117: Large Language Models and Applications
Large Language Models (LLMs) have enabled powerful applications across various domains. In this module, you will learn key technologies, architectures, training and evaluation methods of LLMs, for example, GPT and BERT models. You will also learn the practical use cases and emerging trends of LLM applications. You will be able to analyse real-world problems and formulate effective solutions using LLMs, such as chatbots and machine translation. You will attend lecture and lab sessions, where you will learn to apply and analyse LLMs for your chosen application(s). This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and machine learning.
This module aims to provide you with knowledge and skills to understand, analyse and apply LLMs, including for example, their architectures, training techniques, and practical applications. You will study key topics such as transformer models, GPT and BERT models, and fine-tuning. In this module you will also examine the use of LLMs in real-world scenarios, for example, text generation, text summarisation, and machine translation. You will work with relevant AI frameworks and tools, such as LLMs APIs, to develop LLM applications. Additionally, you will engage with ethical challenges and best practices in deploying LLMs. By the end of the module, you will gain hands-on experience in developing, applying and evaluating LLMs across a variety of domains.
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.
ECMM426: Computer Vision
How do we recognise objects and people? How can we catch a ball or navigate a busy room without collisions? These everyday tasks have challenged AI scientists for decades. Recent advances in computer vision have led to major improvements in applications such as face detection, body tracking, autonomous vehicles, and action recognition.
This module introduces the fundamentals of computer vision, covering both classical and state-of-the-art methods. You will gain a theoretical understanding of key algorithms, along with practical skills in image processing, feature extraction, object detection, segmentation, and deep learning for vision tasks. The course also explores 3D vision and modern topics such as video analysis and low-shot learning, providing a broad foundation for solving real-world vision problems.
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
Funding
We are proud to offer a range of scholarships to help fund your studies. We constantly add to our scholarship portfolio, many of which are funded by our alumni and supporters.
Our prestigious, merit-based Excel at Exeter scholarship is designed to support international applicants with outstanding academic records. We have awards for undergraduate and postgraduate applicants.
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
You will be taught by a range of staff with relevant expertise and knowledge in generative artificial intelligence, Large Language Models (LLMs), and machine leaning. This will include senior academic staff, qualified professional practitioners, demonstrators, technicians and research students. You may also benefit from guest lectures from industry.
Teaching
Teaching is mainly delivered by lectures, workshops and online materials. Each module references core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.
Facilities
We have invested heavily in state-of-the-art teaching facilities for Computer Science and related programmes during 2024. The Lovelace lab seats 120 students and is designed to ensure lines of sight and audio are optimised wherever you are in the room.
The Babbage lab seats 60 and benefits from the same design principles, but also benefits from a breakout space, meaning students can experience both ‘chalk and talk’ teaching of theoretical aspects, and try out these fundamentals in the lab in the same session.
Internationally recognised research
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, becoming actively involved in a research project yourself.
Supportive environment
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. We operate an open-door policy, so it’s easy to consult individual members of staff or to fix appointments with them via email. As a friendly group of staff, you’ll get to know us well during your time here.
Assessments
The assessment strategy for each module is explicitly stated in the full module descriptions given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods include essays, closed book tests, exercises in problem-solving, use of the web for tool-based analysis and investigation, mini-projects, extended essays on specialized topics, and individual and group presentations.
Academic support
Teaching staff include scientists and leading academics in the field who work with businesses to solve difficult, real-world problems. The university also has a growing number of apprenticeship programmes whose direct links with industry benefit the department.
Careers
New roles, such as Generative AI Architect and LLM Engineer, have emerged with the mainstream development of Generative AI, and employers are already looking for the sorts of skills that our graduates will develop. This is the case across all sectors including health, environment, marketing, finance, and the creative industries. Professional certifications and credentials in Generative AI have seen a significant increase in demand in the job market, e.g., the NVIDIA Generative AI Multimodal and LLMs certification. These skills not only enhance employability but also provide a competitive edge, positioning graduates to excel in these rapidly evolving roles. There is significant scope for industry partnerships in respect of case studies, placements, projects etc.
More generally, Computer Science and its related disciplines are at the forefront of technology and innovation. With connectivity at the heart of society, we need graduates who can understand the latest techniques and technology to negotiate problems. There are huge opportunities for businesses and individuals who can solve these problems using cutting edge technologies such as artificial intelligence, machine learning, data science, high performing computing and cyber security.
A Generative Artificial Intelligence degree provides you with a strong foundation in Artificial Intelligence, Machine Learning, and Generative AI. These skills are highly sought after in most sectors, including both public and private, and demands is only increasing.
Graduate destinations
Career opportunities are limitless, with computer science and related graduates being found in a variety of sectors, including software engineering, health communications, education, life sciences, finance and manufacturing. This programme is particularly suited to professionals and graduates looking to develop career options or pursue academia.
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.
