Advanced Computer Science (Artificial Intelligence) MSc

Course overview

From software agents used in networking systems to autonomous vehicles, intelligent systems are becoming integral to modern society – and demand is only going to grow across industries. That’s why many organisations worldwide are looking to skilled experts in AI to provide them with solutions that make their businesses more streamlined.

Our Advanced Computer Science (Artificial Intelligence) MSc will equip you with specialist knowledge and a technical skill set in this fast-moving field whilst allowing you to explore a range of relevant topics in computer science.

You’ll gain a broad perspective on intelligent systems, covering evolutionary models, statistical and symbolic machine learning algorithms, qualitative reasoning, image processing, language understanding and bio-computation, as well as essential principles and practices in the design, implementation and usability of intelligent systems.

Studying in our School of Computer Science gives you access to a whole range of specialist facilities, whilst being taught by academics who are experts in their fields. We’re also responsible for producing internationally excellent research and have long-established links with industry meaning you’ll be learning the most up-to-date practices and techniques needed to pursue an exciting career in industry.

Why study at Leeds:

• Our globally-renowned research conducted right here in our School feeds directly into the course, shaping your learning with the latest thinking in computer science.
• Benefit from studying at a university that’s partnered with the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.
• Advance your knowledge and skills in key areas of computing and intelligence systems like knowledge representation and reasoning and machine learning.
• Tailor the degree to suit your specific interests with a selection of optional modules to choose from such as advanced data science, advanced software engineering and data mining.
• Build industry experience by conducting your own individual project which focuses on a real-world topic of your choice, giving you the chance to develop professional skills in research and critical thinking.
• Access a wide range of industry-standard specialist facilities including a state-of-the-art cloud computing lab, a large High Performance Computing (HPC) resource and a visualisation lab including a Powerwall, benchtop display with tracking system, WorldViz PPT optical tracking system and Intersense InertiaCube orientation tracker.
• Experience expert theoretical and practical teaching delivered by a programme team made up of academics who specialise in a wide range of computing topics.
• Study in the Sir William Henry Bragg building which provides excellent facilities and teaching spaces for an outstanding student experience.

Study online

We also offer a fully online Artificial Intelligence MSc and Postgraduate Certificate, covering an extensive range of AI and machine learning tools and techniques and designed for professionals seeking to develop expertise in this area.

Course details

The first half of the year will lay the foundations of the programme by giving you an understanding of the key topics of algorithms and systems programming, as well as the basic principles of automated reasoning, machine learning and how computers can be made to represent knowledge.

From there you’ll have the chance to tailor your studies to suit your own preferences. You’ll choose from a wide range of optional modules on diverse topics such as image analysis, cloud computing, graph theory and developing mobile apps.

During the second half of the year, over the summer months, you’ll also work on your research project. This gives you the chance to work as an integral part of one of our active research groups, focusing on a specialist topic in computer science and selecting the appropriate research methods.

Course structure

The list shown below represents typical modules/components studied and may change from time to time. Read more in our terms and conditions.

For more information and a full list of typical modules available on this course, please read Advanced Computer Science (Artificial Intelligence) MSc in the course catalogue.

Compulsory modules

Knowledge Representation and Reasoning – 15 credits

You’ll learn how to analyse descriptions of complex real-world scenarios in terms of formal representation languages, understanding automated reasoning and ontology as well as their applications.

Machine Learning – 15 credits

On completion of this module, you should be able to: list the principal algorithms used in machine learning and derive their update rules; appreciate the capabilities and limitations of current approaches; evaluate the performance of machine learning algorithms; use existing implementation(s) of machine learning algorithms to explore data sets and build models.

Deep Learning – 15 credits

This module will equip you with a state-of-the-art understanding of Deep Learning, and highly practical skills and expertise in the construction of AI systems, including those that integrate multiple modalities.

Research Project – 60 credits

The research project is one of the most satisfying elements of this course. It allows you to apply what you’ve learned to a piece of research focusing on a real-world problem, and it can be used to explore and develop your specific interests. Some projects are formally linked to industry and can include spending time at the collaborator’s site over the summer.

Optional modules

Please note: The modules listed below are indicative of typical options.

Data Science – 15 credits

The aim of the module is for you to understand methods of analysis that allow people to gain insights from complex data. You’ll cover the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

Cloud Computing Systems – 15 credits

The module aims to develop a practical understanding of methods, techniques and architectures needed to build big data systems, so that knowledge can be extracted from large, diverse data sets. This module is supported by the strong research interest and expertise in cloud and related technologies within the School of Computer Science. You’ll develop expertise in cloud computing and big data systems as you’re taught the skills and knowledge to design, build and extend the Internet infrastructure and to design a variety of applications.

Blockchain Technologies – 15 credits

This module provides a comprehensive knowledge on fundamentals and practical aspects of distributed ledgers and their applications in society. Starting from required knowledge on distributed systems and security, this module moves to the “big picture” of the different blockchain architectures that have been evolving in this dynamic technological landscape.

Bio-Inspired Computing – 15 credits

During this module, you’ll consider examples of cooperative phenomena in nature and the concepts of emergence and self-organisation. On completion of this module, you’ll have: designed and applied simple genetic algorithms; interpreted the behaviour of algorithms based on the cooperative behaviour of distributed agents with no, or little, central control; and implemented bio-inspired algorithms to solve a range of problems.

Algorithms – 15 credits

Algorithms and algorithmic problem solving are at the heart of computer science. This module introduces the design and analysis of efficient algorithms and data structures. You'll learn how to quantify the efficiency of an algorithm and what algorithmic solutions are efficient. You’ll also be taught techniques for designing efficient algorithms, including efficient data structures, standard methods such as Divide-and-Conquer and Dynamic Programming as well as more advanced techniques. This is done using illustrative and fundamental problems relevant to AI.

Programming for Data Science – 15 credits

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by online courseware.

Data Mining and Text Analytics – 15 credits

This module will provide you with an introduction to linguistic theory and terminology. On competition you should be able to: demonstrate an understanding and how to use algorithms and resources for implementing and evaluating text mining and analytics systems; develop solutions using open-source and commercial toolkits; and consider the applications of data mining and text analytics through case studies in information retrieval and extraction.

Advanced Software Engineering – 15 credits

In this module, you’ll build on prior knowledge of software engineering principles, expanding it to include a more thorough understanding of what constitutes good design. You’ll learn how design can be improved using patterns and refactoring, and you’ll gain a broad appreciation of the different architectural styles used in modern software.

Scientific Computation – 15 credits

This module will support your understanding of the range of problems that can be formulated as nonlinear equation systems. On completion, you should be able to: consider standard algorithms for these problems and the efficiency of their implementation; and demonstrate how state-of-the-art algorithms deliver gains in efficiency and allow the solution of large, sparse systems of nonlinear equations.

Graph Theory: Structure and Algorithms – 15 credits

Graphs are an extremely powerful tool for modelling real-world systems, with applications in logistics, telecommunication, molecular biology, industrial engineering, linguistics, chemistry, and many other areas. Many of the optimisation problems arising from these applications are computationally difficult when there are no restrictions on the input. However, the scenarios that we aim to model often impose additional conditions on the structure of the corresponding graphs. This module focuses on how that structural information can be used to solve the relevant optimisation problems efficiently, with an emphasis on mathematical precision.

Learning and teaching

Our groundbreaking research feeds directly into teaching, and you’ll have regular contact with staff who are at the forefront of their disciplines. You’ll be taught through lectures, seminars, tutorials, small group work and project meetings.

Independent study is also important to the programme, as you develop your problem-solving and research skills as well as your subject knowledge.

Specialist facilities

At Leeds, we provide an exciting environment in which to gain a range of skills and experience cutting-edge technology.

You’ll benefit from UK-leading facilities to support your learning, including:

• A state-of-the art computing cluster, with access to Azure services and GPU computing as required by your modules 
• High-performance graphics workstations, equipped with modern software libraries and tools for virtual reality and real time visualisation and interaction
• Robotics labs
• Dedicated Linux laboratories with a combined capacity of an average of 150 machines
• Excellent facilities and teaching spaces in the Sir William Henry Bragg building.

Programme team

On this course, you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.

Assessment

You’ll be assessed using a range of techniques which may include case studies, technical reports, presentations, in-class tests, assignments and exams. Optional modules may also use alternative assessment methods.

Applying

Entry requirements

A bachelor degree with a 2:1 (hons) in computer science. We require all applicants to have studied a breadth of relevant modules including significant programming, systems development, data structures and algorithms, with strong marks across all these modules.

Applicants with any of the following will be considered on a case-by-case basis:

• A bachelor degree with a 2:1 (hons) in other Computing-based degrees
• Professional qualifications and relevant experience.

International

We accept a range of international equivalent qualifications. For more information please contact the Admissions Team.

English language requirements

IELTS 6.5 overall, with no less than 6.0 in any component.

Improve your English

International students who do not meet the English language requirements for this programme may be able to study our postgraduate pre-sessional English course, to help improve your English language level.

This pre-sessional course is designed with a progression route to your degree programme and you’ll learn academic English in the context of your subject area. To find out more, read Language for Engineering (6 weeks) and Language for Science: Engineering (10 weeks). 

We also offer online pre-sessionals alongside our on-campus pre-sessionals. Find out more about our six week online pre-sessional.

You can also study pre-sessionals for longer periods – read about our postgraduate pre-sessional English courses.

How to apply

Application deadlines

Please read our How to Apply page for full details, including application deadlines and what to include with your application.

Applicants are encouraged to apply as early as possible.

30 June 2025 – International applicants

12 September 2025 – UK applicants

Click below to access the University’s online application system and find out more about the application process.

If you're still unsure about the application process, contact the admissions team for help.

Read about visas, immigration and other information in International students. We recommend that international students apply as early as possible to ensure that they have time to apply for their visa.

Admissions policy

University of Leeds Admissions Policy 2025

This course is taught by

School of Computer Science

Contact us

Postgraduate Admissions team

Email: pgcomp@leeds.ac.uk
Telephone:

Fees

UK: £14,250 (Total)

International: £33,750 (Total)

Read more about paying fees and charges.

For fees information for international taught postgraduate students, read Masters fees.

Additional cost information

There may be additional costs related to your course or programme of study, or related to being a student at the University of Leeds. Read more on our living costs and budgeting page.

Scholarships and financial support

If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There may be help for students in the form of loans and non-repayable grants from the University and from the government.  Find out more at Masters funding overview.

Career opportunities

Computing is an essential component of nearly every daily activity, from the collection and processing of information in business, through to smart systems embedded in devices, image processing in medical diagnosis and the middleware that underpins distributed technologies like cloud computing and the semantic web.

This programme will give you the practical skills to enter many areas of applied computing, working as application developers, system designers and evaluators. Links between the taught modules and our research provide our students with added strengths in artificial intelligence, intelligent systems, distributed systems, and the analysis of complex data. As a result, you’ll be well-prepared for a range of careers, as well as further research at PhD level.

Plus, the University of Leeds is in the top 5 most targeted universities in the UK by graduate recruiters, according to High Fliers’ The Graduate Market in 2024 report.

Here’s an insight into some of the job positions and organisations previous advanced computer science graduates have secured:

• Transaction Risk Investigator, Amazon Development Centre, India
• Engineer, Johns Hopkins University Applied Physics Laboratory
• Senior Software Engineer, Funding Circle
• Technical Developer, Reading Room
• Head of Audience Development, Al Jazeera Media Network
• Systems Engineer, Systematic
• Software Engineer, THG
• Programmer, Alibaba

Careers support

At Leeds, we help you to prepare for your future from day one. We have a wide range of careers resources — including our award-winning Employability Team who are in contact with many employers around the country and advertise placements and jobs. They are also on hand to provide guidance and support, ensuring you are prepared to take your next steps after graduation and get you where you want to be.

• Employability events — we run a full range of events including careers fairs in specialist areas and across broader industries — all with employers who are actively recruiting for roles.
• MyCareer system — on your course and after you graduate, you’ll have access to a dedicated careers portal where you can book appointments with our team, get information on careers and see job vacancies and upcoming events.
• Qualified careers consultants — gain guidance, support and information to help you choose a career path. You’ll have access to 1-2-1 meetings and events to learn how to find employers to target, write your CV and cover letter, research before interviews and brush up on your interview skills.
• Opportunities at Leeds — there are plenty of exciting opportunities offered by our Leeds University Union, including volunteering and over 300 clubs and societies to get involved in.

Explore more about your employability opportunities at the University of Leeds.

Find out more about career support.