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 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).
Core modules -Investigating Research -Researcher Professional Development
Optional modules - students select three modules from the following: -Advanced Topics in Machine Learning -Statistical Inference -Applied Bayesian Methods -Approximate Inference and Learning in Probabilistic Models -Graphical Models -Information Retrieval and Data Mining -Inverse Problems in Imaging -Machine Vision -Probabilistic and Unsupervised Learning -Statistical Computing -Statistical Inference -Statistical Models and Data Analysis -Supervised Learning -Selected Topics in Statistics
Dissertation/report 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.
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.
Employability 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.
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.
Selection criteria: The bursary will be awarded based on financial need only as determined by the Student Funding Office.Value, Benefits and Duration: - The value of the bursary is £15,000. The payment is to be applied to tuition fees in the first instance, with any remainder being paid to the successful applicant towards maintenance in termly instalments. - The bursary is tenable for one year only. - The bursary may not be held alongside other tuition fee only awards.
Value of Scholarship(s)
Applicants must be: - prospective full-time UK Master's students who are undertaking a one-year programme of study in the Faculties of the Built Environment, Engineering Sciences or Mathematical & Physical Sciences (BEAMS) in 2015/16 - currently holding a first-class Bachelor's degree; and - in financial need.
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.
Recipient: University College London
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