Artificial Intelligence MSc

An overview of the degree and advice on selecting your courses.

Overview of the degree

Edinburgh hosts the UK's longest established centre for artificial intelligence, which remains one of the best in the world. Courses on this degree span a wide range of areas in artificial intelligence and also draw on research in related fields such as neuroscience, cognitive science, linguistics, and mathematics. We aim to give you the fundamental knowledge and practical skills needed to design, build, and apply AI systems in your chosen area of specialisation.

Full-time students on this degree must be registered for exactly 180 credit points at all times.  Part-time students have the same course requirements, but spread out over two or three years.

Compulsory courses

The following courses are compulsory:

  • Informatics Research Review (IRR) — 10 credits of coursework in Semester 1.
  • Informatics Project Proposal (IPP) — 10 credits of coursework in Semester 2.
  • MSc Dissertation — 60 credits in the Summer.

Information about IRR, IPP, and Dissertation

Optional courses

In addition, you must register for another 100 credits of optional taught courses, usually split evenly across two semesters, with at least 60 credits chosen from AI and Cognitive Science courses, and the remainder from other Informatics and outside courses.

This degree offers considerable flexibility in your taught course choices. Specific course options and requirements are listed in your Degree Programme Table, and are built into the Path programme builder, which you can use to help you put together a set of courses that works for you.

Degree Programme Tables

Path programme builder

Course topics and advice on choosing courses

Course topics are groups of related courses to help you navigate our course options. The topics most relevant to this degree are described below.

Programming courses

Before finalizing your other courses, please check the advice on the Programming Courses topic page to see whether you would benefit from taking one of these courses to improve your programming skills.

Programming Courses

AI course topics

We recommend that you pick at least 60 credits of your courses from one or two of the AI topics listed below. This will allow you to get some depth in at least one topic, as well as some breadth. If you pick just one AI topic, it's still a good idea to use your non-AI credits to take a course or two from another topic. This will give you some flexibility when it comes to project selection time, since sometimes projects in one particular topic may be oversubscribed. Your Personal Tutor can also help advise you about your selection of courses if you're unsure.

Please consider the prerequisites for each course before signing up. Not all students will be prepared for all available courses, but you should be able to find a selection that works for you. Courses in some topics tend to require more mathematics, while others may require more programming or other skills. The individual topic pages often provide some general guidance about this, but check the course descriptors if you're unsure.

For more information about the courses in each topic, follow the links below.

Natural Language Processing

Machine Learning

Vision, Robotics & Autonomous Agents

Closely related topics

The following topics are not usually considered part of AI, but courses in these topics often have strong links to AI and can be a good complement to courses from the AI topics.

Bioinformatics, Systems & Synthetic Biology

Cognitive Science & Neuroinformatics

Databases and Data Management

Other topics

You may also choose from other courses in Informatics or other Schools. Most courses taken by our students (including some popular outside courses) are listed in the Topic pages. The complete list of courses offered by the School is in the Sortable Course List.

Full list of Course Topics (information about most MSc courses, according to topic)

Informatics Sortable Course List (a compact list of all courses offered this year, sortable by semester, level, credits, etc.)