The MSc in Artificial Intelligence is a mathematics-focused course that introduces the core principles behind developing intelligence in computers and machines, with the aim of creating AI that can learn from experience.
Artificial intelligence underpins many of the technologies shaping our everyday lives and is one of the most exciting areas of computer science.
You’ll explore how AI systems can derive implicit knowledge from data, interpret natural languages such as English, Arabic or Urdu, recognise and analyse images, and ultimately collaborate with humans. The techniques covered are as diverse as the problems they address - ranging from classical logic to statistical methods that simulate aspects of the human brain.
Teaching is organised around four themes, each comprising two course units. Three of the themes are mandatory:
- Fundamentals of Machine Learning
- Symbolic AI
- AI Applications
You’ll then tailor your studies by choosing a fourth theme from a selection of optional areas, including data engineering, software engineering, and cyber security.
A highlight of the course is the Master’s Project, where you’ll carry out a substantial technical challenge in an area that interests you. You’ll choose from a portfolio of specialised AI projects, applying and deepening the techniques learned across the taught units.
As a graduate, you’ll leave with advanced technical skills and cutting-edge knowledge of emerging technologies.
Course duration 12 months (full-time)
Total self-study time Approx. 30 hrs/week (taught units) in semester 1 and 2 | 40 hrs/week (Master’s project) in the summer
Teaching time Approx. 12 hours per week (inc. pre-recorded and live lectures, and practical classes)
WHAT CAREER PATHWAYS ARE AVAILABLE TO ME?
The MSc in Artificial Intelligence is an excellent way to develop specialised knowledge in modern computer science. It suits both those seeking a practical career in industry and those keen to pursue industrial or academic research opportunities.
As a graduate of this course, you will enter the world of work with sought-after advanced technical skills and an enviable knowledge of emerging technologies and techniques. We prepare students to thrive in the rapidly evolving tech landscape and make a lasting impact on society.
You’ll benefit from the wide career options available to those studying the Advanced Computer Science pathway, while also being uniquely positioned for roles requiring expertise in AI formalisms and technologies across industries such as finance, healthcare, telecommunications, and beyond.
ROLES GRADUATES CAN SECURE
As you graduate, you not only benefit from the many career options open to those who study the Advanced Computer Science pathway, but are ideally placed to work in positions requiring an understanding of modern AI formalisms and technologies - such as Natural Language Processing and Machine Learning. This includes roles in the games industry, finance, commerce, and scientific research, and many more.
We anticipate graduates to secure roles such as:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Software Developer
- Business Intelligence Analyst
- Project Manager
…and many more.
OUR STUDENTS' MSc PROJECTS
The MSc project is one of the most exciting parts of a master’s degree — you are paired with an expert supervisor and get a chance to dive deep into a topic you’re passionate about, solve real-world problems, and bring your ideas to life. It’s where everything you’ve learned comes together, showcasing your skills, creativity, and potential to stand out in your field. Here are some projects that our MSc graduates have worked on:
🔎 Knowledge graph validation and reasoning
The Resource Description Framework (RDF) and other kinds of knowledge graphs were used and promoted as an "intelligent" and flexible alternative to classical database models. In this project, students explored RDF as a knowledge graph formalism, using an OWL reasoner to infer new knowledge from the existing knowledge in a knowledge graph and a SHACL schema to express constraints. Determining how to combine such a reasoner and validator proved to be a non-trivial task, as the computations involved could be complex and depended on the order in which reasoning and validation were performed.
🔎 Physics-Informed Robot Learning This project built a physics-informed probabilistic model for robot learning using a Credal Bayesian Deep Learning (CBDL) approach, in which a user trained a Bayesian Neural Network (BNN) using credal sets of priors and likelihoods instead of single distributions.
🔎 Provenance of information in LLMs Large Language Models (LLMs) are built from massive amounts of textual data, but it is often unclear where that data comes from. The goal of this project was to investigate whether it was possible to identify the sources of the data used in an LLM’s response. For example, if ChatGPT was asked to write something in the style of Seamus Heaney, Nobel laureate and Irish poet, the project explored whether the memorized sources that informed the response could be found.
Professor Uli Sattler [ROLE]
ACADEMIC'S VOICE
Our new AI course is designed to give our students a broad and deep understanding of relevant topics in AI, including Machine Learning, Symbolic AI, and AI applications. In addition, students will gain skills necessary to analyse AI models and their performance, to use symbolic approaches to enhance trustworthiness of AI systems, and to use AI systems for challenging applications.
ALUMNI'S VOICE
After completing my master’s degree at The University of Manchester, I worked as a Machine Learning Researcher at Alliance Manchester Business School in partnership with Forensic Testing Services , where I led the development of a scalable machine learning platform to predict drug use from forensic data using NLP and text mining. Currently, I am a Research Fellow at King’s College London, working on CogStack, a healthcare NLP platform designed to extract insights from unstructured clinical data such as Electronic Health Records. My work involves developing MedCAT, a medical concept annotation tool that links clinical text to biomedical ontologies in collaboration with the NHS.
I’m now preparing to begin a PhD in Biostatistics and Health Informatics at King's College London. The University of Manchester played a crucial role in shaping this path, providing a strong foundation in applied machine learning and interdisciplinary research that continues to guide my work.
Shubham Agarwal
Artificial Intelligence, Class of 2022 Current role: Research Fellow, King's College London, London, United Kingdom
Ushasree Sanyal
Advanced Computer Science, Class of 2020 Current role: Senior Data Scientist, EPAM Systems, Warrington, United Kingdom
ALUMNI'S VOICE
I began my career as a Software Engineer in 2016 but soon developed a keen interest in AI, which led me to pursue an MSc in Advanced Computer Science with a specialization in AI, at The University of Manchester. The course provided a strong foundation in AI and machine learning, enabling my transition into data science in 2020. Since then, I’ve progressed from Junior Data Scientist to Data Scientist, and now I’m a Senior Data Scientist at EPAM, where I’ve been since 2021. A key highlight has been leading a Generative AI project, where I contributed to designing a platform to support decision-making for a trading firm. My master’s degree was instrumental in shaping my technical expertise and career progression.
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