Taught course

Computational Statistics and Machine Learning

UCL - University College London · Computer Science

Entry requirements

A minimum of an upper second-class UK Bachelor's degree in a highly quantitative subject, or an overseas qualification of an equivalent standard. We require candidates to have studied a significant mathematics and/or statistics component as part of their first degree, and students should also have some experience with a programming language, such as MATLAB. If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency. The English language level for this programme is: Good. Further information can be found on our English language requirements http://www.ucl.ac.uk/prospective-students/graduate/life/international/english-requirements page.

Months of entry


Course content

There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. MRes graduates benefit from the department\'s excellent links in finding employment; this programme is also ideal preparation for a research career.

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning, having coordinated the PASCAL European Network of Excellence.

UCL CSML is a major European centre for machine learning, having organised the PASCAL European Network of Excellence which represents the largest network of machine learning researchers in Europe.

UCL Computer Science graduates are particularly valued by the world\'s leading organisations in internet technology, finance, and related information areas, as a result of the department\'s strong international reputation and ideal location close to the City of London.

1 year;

Qualification and course duration


full time
12 months


The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with assistance from demonstrators. Students liaise with their academic or industrial supervisor to choose a study area of mutual interest for the research project. Performance is assessed by unseen written examinations, coursework and the research dissertation.

Course contact details

+44 (0)20 3370 1214