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.
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.
Recent projects for Advanced Computer Science (Artificial Intelligence) MSc students have included:
Object-based attention in a biologically inspired network for artificial vision
Advanced GIS functionality for animal habitat analysis
Codebook construction for feature selection
Learning to imitate human actions
A proportion of projects are formally linked to industry and can include spending time at the collaborator’s site over the summer.
Want to find out more about your modules?
Take a look at the Advanced Computer Science (Artificial Intelligence) module descriptions for more detail on what you'll study.
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
Knowledge Representation and Reasoning
Optional modules (selection of typical options shown below)
Programming for Data Science
Data Mining and Text Analytics
Advanced Software Engineering
Graph Theory: Structure and Algorithms
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.
You’ll benefit from world-class facilities to support your learning, including:
a state-of the art cloud computing lab with a 10-node cluster
a large High Performance Computing (HPC) resource consisting of several clusters which are used for all forms of predictive modelling, data analysis and simulation
a visualisation lab including a Powerwall, benchtop display with tracking system, WorldViz PPT optical tracking system and Intersense InertiaCube orientation tracker
Ascension Flock of Birds tracking systems, three DOF and 6DOF Phantom force feedback devices
Twin Immersion Corp CyberGloves
rendering cluster and labs containing both Microsoft and Linux platforms, among others.
You'll study in the Sir William Henry Bragg building, a brand-new development providing excellent facilities and teaching spaces for an outstanding student experience.
It’s an exciting environment in which to gain a range of skills and experience cutting-edge technology.
Our Virtual Learning Environment will help to support your studies: it’s a central place where you can find all the information and resources for the School, your programme and modules.
You can also benefit from support to develop your academic skills, within the curriculum and through online resources, workshops, one-to-one appointments and drop-in sessions.
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. View the course pages for more details.
Programme leader, Dr Brandon Bennett, research interests include reasoning about spatial relations and physical systems, reasoning with vague concepts, semantics of actions and events and representation using non-classical Logics Automated deduction.
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.
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.