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.
Modes and duration
Tuition fees (2017/18)
- £11,800 (FT)
- £25,130 (FT)
Note on fees: The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Current Students website.
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.
English language requirements
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 page.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
International applicants can find out the equivalent qualification for their country by selecting from the list below.
Select your country:
About this degree
The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the areas of computational statistics and machine learning (CSML). Students will have the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research. They also undertake a nine-month research project which enables the department to more fully assess their research potential.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), three optional modules (45 credits) and a dissertation/report (105 credits).
- Investigating Research
- Researcher Professional Development
Student select three modules from the following:
- Advanced Deep Learning and Reinforcement Learning
- Advanced Topics in Machine Learning
- Applied Bayesian Methods
- Approximate Inference and Learning in Probabilistic Models
- Graphical Models
- Information Retrieval and Data Mining
- Introduction to Deep Learning
- Introduction to Machine Learning
- Inverse Problems in Imaging
- Machine Vision
- Probabilistic and Unsupervised Learning
- Selected Topics in Statistics
- Statistical Computing
- Statistical Inference
- Statistical Models and Data Analysis
- Supervised Learning
All students undertake an independent research project which culminates in a substantial dissertation.
Teaching and learning
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.
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, and Chicago, as well as at UCL. Similarly, CSML graduates now work in companies in Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in CSML graduates.
Scientific experiments and companies now routinely generate vast databases, and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally, while in London there are many companies looking to understand their customers better who have hired CSML graduates. Computational statistics and machine learning skills are in particular demand in areas including finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. CSML graduates have obtained PhD positions both in machine learning and related large-scale data analysis, and across the sciences.
Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2012–2014 graduating cohorts six months after graduation.
Why study this degree at UCL?
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.
Department: Computer Science
Student / staff numbers
› 200 staff
including 120 postdocs
› 650 taught students
› 180 research students
Staff/student numbers information correct as of 1 August 2017.
Research Excellence Framework (REF)
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Computer Science
96% rated 4* (world-leading) or 3* (internationally excellent)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
Application and next steps
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Who can apply?
Students are expected to have a strong background in a numerate subject, ideally mathematics, statistics or computer science. The MRes is particularly suitable for students who have some prior familiarity with data analysis and wish to engage in a substantial research project, prior to progressing to a research career.
- All applicants
- 17 June 2017
For more information see our Applications page.Apply now
What are we looking for?
When we assess your application we would like to learn:
- why you want to study Computational Statistics and Machine Learning at graduate level
- why you want to study Computational Statistics and Machine Learning at UCL
- what particularly attracts you to this programme
- how your academic and professional background meets the demands of this programme
- what mathematics and statistics experience you have
- what programming experience you have
- where you would like to go professionally with your degree
- we also ask that students attach a formal research proposal with their application
- n your application, please state the name of an academic that you would wish to supervise your MRes project
Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver. Applicants who have a portfolio are strongly recommended to submit it when they apply.
Successful applicants to this programme will be required to pay a tuition fee deposit dependent on their mode of study and fee status as given below:
- UK/EU full-time: £2,000
- Overseas full-time: £2,000
Further details can be found on the Fees and funding page.