The courses, (or modules), that make up your degree programme will vary from the very theoretical to the very practical, but a ‘typical’ course might include 30 hours of lectures and ten hours of practical classes. You will need to do a lot of work in your own time, however you will be able to do much of your practical work when it suits you best.

You will complete coursework for the majority of your modules and will take your exams at the end of each year.  All of your grades will count towards your degree, with the project having the highest weighting of all of your modules. 

The MSc in Machine Learning is one of the few top master programmes that is entirely dedicated to machine learning. It combines a core component and a flexible part that you can tailor to your specific interests. The core component is designed to deliver the advanced knowledge and impart valuable technical skills. The electives offer further advanced taught components which build on the core structure. 

The course is split into core and optional modules. Students must take 4 core modules plus 4 additional modules from the 'optional modules' list They will also complete an individual project.

Module options are as follows

The following lecturers are among those who teach modules for the MSc ML:

John Shawe-Taylor
John is the Head of the Computer Science Department, and was former Director of the Centre for Computational Statistics and Machine Learning. His main research area is Statistical Learning Theory. He is currently the Scientific Coordinator of a Framework 7 Network of Excelence in Pattern Analysis, Statistical Modelling and Computational Learning 2 (PASCAL2).
John's Homepage

David Barber
David is the Director of the MSc in Computational Statistics and Machine Learning. His main research interests are in Information Processing in complex systems ans machine learning.
David's Homepage

Mark Herbster
Mark is the Director of the MSc in Machine Learning. His research focuses on the problem of predicting a labelling of a graph (network).
Mark's Homepage

Massimiliano Pontil
Massimiliano is a Professor in the Computer Science department. His main research interests are machine learning theory and pattern recognition.
Massimiliano's Homepage

Yee Whye Teh
Yee Whye is a lecturer in the Gatsby Unit. His main research interests are Statistical Machine Learning and its applications.
Yee Whye's Homepage

David Jones
David is the Director of the Bloomsbury Centre for Bioinformatics. His main areas of interest include protein structure prediction and analysis, simulations of protein folding, Hidden Markov Model methods, transmembrane protein analysis, machine learning applications in bioinformatics, de novo protein design methodology, and genome analysis.
David's Homepage

Simon Prince
Simon is a Senior Lecturer in the Computer Science Department, and is part of the Vision and Imaging Science Group. His research interests include the use of Bayesian methods in computer vision, particularly for face recognition and scene parsing.
Simon's Homepage

Jun Wang
Jun is a lecturer in the Computer Science department. His research interests include information retrieval, multimedia content analysis and statistical pattern recognition.
Jun's Homepage

Kevin Bryson
Kevin is a lecturer in the Computer Science department, and is part of the Bioinformatics group. Kevin's research covers Bioinformatics and System Biology problems applied to Stem Cells.
Kevin's Homepage

Maneesh Sahani
Maneesh is a lecturer in the Gatsby unit. His research covers theoretical neuroscience, neural data analysis and machine learning.
Maneesh's Homepage

Nadia Berthouze
Nadia is a Senior Lecturer in the UCL Interaction Centre (UCLIC). The premise of Nadia's research is that affect, emotion, and subjective experience should be factored into the design of interactive technology. Indeed, for technology to be truly effective in our social network, it should be able to adapt to the affective needs of each user group or even each individual. The aim of her research is to create systems/software that can sense the affective state of their users and use that information to tailor the interaction process.
Nadia's Homepage

Simon Arridge
Simon is Head of the Image processing group in the Department of Computer Science.
Simon's Homepage

• Message from John Shawe-Taylor, Head of Computer Science Department, and former director of the CSML Centre

’The last decade has seen the emergence of a dynamic research agenda that bridges traditional statistics and the more recent topic of machine learning. A rapidly expanding toolkit of algorithms and analysis techniques has enabled a new generation of researchers to convert raw data into useful insights and knowledge in domains ranging from scientific disciplines like bioinformatics to commercial applications such as credit rating. The Centre for Computational Statistics and Machine Learning is a pioneer in this emerging field that brings together statistics, the recent extensive advances in theoretically well-founded machine learning, and links with a broad range of application areas drawn from across UCL, including neuroscience, astrophysics, biological sciences, complexity science, etc. The MScs offered by the centre (MSc Machine Learning and MSc CSML) will give students an understanding of the range and applicability of these new methods, hence equipping them to satisfy the rapidly expanding demand for these skills in commerce and industry, or alternatively preparing to launch themselves on a research career in this area.’

• Message from Mark Herbster, MSc Machine Learning Programme Director

’Machine learning is a sub-discipline of computer science which studies the process of automatic inference from data. This field draws on ideas and methods from a diversity of perspectives and disciplines such as artificial intelligence, connectionism, optimization, pattern recognition, and statistics. The commercial successes of machine learning are widespread, some well-known examples are in speech recognition, robotic vision, online ad-placement, fraud detection, and bioinformatics. At UCL we offer an MSc in Machine learning that is both scientifically rigorous and industrially relevant.

UCL has an internationally recognized faculty in Machine Learning. The faculty are affiliated with two major research centres, the Centre for Computational Statistics and Machine Learning and the Gatsby Computational Neuroscience Unit, the MSc further benefits from close collaboration with the Bioinformatics Group and the Digital Biology.

After six successful years of running the MSc in Intelligent Systems, we split MSc into two tightly interconnected MSc programs:

The first MSc in Machine Learning is aimed at students trained in computer science or another quantitative science. This MSc is designed to train the student in both the practical and theoretical sides of machine learning and is aimed to prepare the student for either an industrial career or for PhD study. The second MSc in Computational Statistics and Machine Learning shares the same goals, but is co-taught with the statistics department and thus provides a significant grounding in computational statistics.’

If you require more information on the course or how to apply, you can contact the following members of staff:

Rebecca Martin, Postgraduate Administrator - for Administrative queries
Email: rebecca.martin (at) cs.ucl.ac.uk
Phone: +44 (0)20 7679 0481
Fax: +44 (0)20 7387 1397

Mark Herbster, Programme Director - for Academic queries
E-Mail: M.Herbster (at) cs.ucl.ac.uk
Phone: +44 (0)20 7679 3684 (preferably at 5-7 pm UK time, i.e. GMT without offset)
Fax: +44 (0)20 7387 1397
Homepage: http://www.cs.ucl.ac.uk/staff/M.Herbster/