MSc Computational Statistics & Machine Learning

Award:  
Master of Science (Honours)
Level:Postgraduate
Intended for:Graduates with experience in Mathematics and/or Statistics
Duration:1 Year
Full/Part Time: Full Time Only
Places:20 per year

Modules Taught: Click Here

Contact:               Rebecca Martin

**Admissions will open for September 2013 entry in November**


Why choose this course?

The MSc in Computational Statistics and Machine Learning 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.

This programme is organised by the Centre for Computational Statistics and Machine Learning (CSML) within the Department of Computer Science. The CSML Centre is a major European Centre for Machine Learning and is scientific coordinator of the PASCAL2 European Network of Excellence. Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and the prestigious Department of Statistical Science, the programme draws on world class research and teaching talents.

The programme is suitable for students with good analytical abilities and is intended as an entrance to a career in either academia or industry. The CSML Centre has excellent links with world-leading companies in internet technology, finance and related information areas. Additionally, the MSc offers a route to embarking on a PhD in machine learning, signal processing, statistics, information processing and retrieval, for which opportunities all exist at UCL.

Information on the Department and how to apply can be found below. You may also wish to browse our course website for current students for additional information:


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Department of Computer Science, UCL (University College London)
Malet Place, London WC1E 6BT, UK
Phone: 020 7679 7214 (+44 20 7679 7214)
Fax: 020 7387 1397 (+44 20 7387 1397)