Computational Statistics and Machine Learning MSc


This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantative finance, artificial intelligence and machine vision.


Mode of study

  • Full-time 1 year

Tuition fees

  • UK/EU Full-time: £10,450
  • Overseas Full-time: £21,700

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 area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Why should I 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.

Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The Centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.


Students undertake modules to the value of 180 credits. The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits).

Core Modules

  • Supervised Learning
  • Statistical Modelling and Data Analysis
  • Graphical Models or Probabilistic and Unsupervised Learning

  • Plus one of:
  • Applied Bayesian Methods
  • Statistical Design of Investigations
  • PLUS ONE OF:
  • Applied Bayesian Methods
  • Statistical Design of Investigations
  • Statistical Computing
  • Statistical Inference

Options

  • Machine Vision
  • Bioinformatics
  • Information Retrieval and Data Mining
  • Forecasting
  • Stochastic Methods in Finance
  • Advanced Topics in Machine Learning
  • Evolutionary and Natural Computation
  • Inverse Problems in Imaging
  • Approximate Inference and Learning in Probabilistic Models
  • Programming and Mathematical Methods for Machine Learning
  • Stochastic Methods in Finance II
  • Advanced Topics in Statistics
  • Applied Machine Learning

Dissertation/report

All MSc students undertake an independent research project, which culminates in a dissertation of 10,000 -12,000 words.

Teaching and Learning

The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Further details available on subject website:


Funding

We are offering four MSc Excellence Scholarships worth £4,000 to UK/EU offer holders with a record of excellent academic achievement. Please note that the closing date for applying for this is 30 June 2014.

Scholarships available for this department

Brown Family Bursary

This award is based on financial need.

Childcare Support Grant

Selection based solely on financial need.

Graduate Support Bursary

For a prospective UK Master's student from under-represented background enrolling on a participating programme . Selection based solely 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 computer science, statistics, mathematics, electrical engineering or the physical sciences; or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Students must be comfortable with undergraduate level mathematics. In particular it is essential that the candidate will have knowledge of statistics at an intermediate undergraduate level. The candidate should also be proficient in linear algebra and multivariable calculus.

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. Applications received after the end of March are less likely to be successful.

Who can apply?

The programme is suitable for students with good analytical abilities. Graduates will benefit directly from our excellent academic and industrial links, either by embarking on further study in a leading centre in machine learning/statistics or by taking up a place in industry, in information retrieval, finance or bioinformatics.

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.


Career

There is a strong national and international demand for graduates with skills at the interface between traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include; finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Top career destinations for this programme

  • Gulf International Bank, Quantitative Analyst, 2011
  • Alchamatics, Director, 2011
  • Markit, VP of Fixed Income Department, 2011
  • Cognitive Match, Researcher, 2011
  • UBS, Trader, 2010

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. CSML graduates have been in high demand for PhD positions across the sciences. There is a considerable shortfall in the number of qualified graduates in this area internationally. In London there are many companies looking to understand their customers better and have therefore hired CSML graduates. Similarly CSML graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in CSML graduates, having hired several recently.


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.


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

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