Computational Statistics and Machine Learning MRes


There is a high demand from industry worldwide, including 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.


Mode of study

  • Full-time 1 year

Tuition fees

  • UK/EU Full-time: £7,500
  • UK/EU Part-time: £TBC
  • Overseas Full-time: £20,900
  • Overseas Part-time: £TBC

Application date

  • All applicants: 1 August 2014

More details in Application section.


What will I learn?

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.

Why should I study this degree at UCL?

The programme is delivered by UCL Centre for Computational Statistics and Machine Learning (UCL CSML), UCL Statistical Science, and the internationally renowned Gatsby Computational Neuroscience Unit and draws on world-class research and teaching talents.

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.


Students undertake modules to the value of 180 credits. The programme consists of 2 core modules (30 credits), 3 optional modules (45 credits) and a dissertation/report (105 credits).

Core Modules

  • Investigating Research
  • Researcher Professional Development

Options

  • Students choose three from the following:
  • Advanced Topics in Machine Learning
  • Advanced Topics in Statistics
  • 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 Modelling and Data Analysis
  • Supervised Learning

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.

Further details available on subject website:


Scholarships available for this department

Brown Family Bursary

This award is based on financial need.

Further information about funding and scholarships can be found on the Scholarships and funding website.


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.

International equivalencies

Select your country for equivalent alternative requirements

English language proficiency level: Standard

How to apply

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.

The deadline for applications is 1 August 2014.

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.

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
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.


Career

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.


Next steps

Contact

Miss Rebecca Martin

T: +44 (0)20 7679 0481

Department

Computer Science

Register your interest

Keep up to date with news from UCL and receive personalised email alerts. Register your interest

Make an application

APPLY HERE


Prospectus subject

Computer Science

Faculty overview

Engineering Sciences


Videos

View videos about UCL and its global impact on our YouTube channels: Study UCL and UCLTV.


Alumni View

"I chose to study at UCL because of the prestige and philosophy of the university. I've been connected to UCL for 14 years. I completed my undergraduate, PhD, and postdoctoral research at the university and have spun-out a company from UCL."

Daniel Hulme

CEO, Satalia, 2008

Staff View

"It is amazing to be in a position to regularly interact and collaborate with the brightest minds of the current generation and together help to shape the future, constructively and positively."

Dr Niloy Mitra

Reader in Geometric Modeling and Computer Graphics