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 course starts with the Statistics Foundation Fortnight, which begins one week before the official start of term. This is a compulsory two week mathematics refresher and test. Students who subsequently feel uncomfortable with the level of mathematics required may transfer to our sister Machine Learning course.

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

The programme is designed to provide a rigorous understanding of Machine Learning and Statistical techniques through the core courses. Optional courses enable the student to utilise their key theoretical skills on relevant applications.

Here are the syllabi for the available courses.

Students must take 2 Computer Science core modules from the following list:

Students must then take 2 Statistics core modules from the following list:

Students then choose 4 of the following optional modules:

The project is a major component of the Masters degree and is a chance to research in some detail an area of interest to the student. The project dissertation counts for 33% of the total marks for the programme. The project is chosen in agreement with the Course Director at the start of the second term, and would nominally be (co)supervised by the Statistics or Computer Science department.

Whilst every effort is made to keep the syllabus and assessment records correct for each module listed above, the precise details should be checked with the lecturer(s) for that module.

Whilst not mandatory, by taking a suitable combination of optional and core courses, natural themes of study arise. Depending on timetable constraints, suggested themes that students may wish to follow are given below:

• Bioinformatics Theme: The optional course pertinent to this theme is currently Bioinformatics. Student projects in this theme are supervised by the Bioinformatics unit.
• Machine Vision Theme: Pertinent courses are Machine Vision and Graphical Models. Student projects are supervised by the Vision and Imaging Science group in the Computer Science department.
• Information Retrieval Theme: Pertinent courses are, Probabilistic and Unsupervised Learning, Approximate Inference and Learning in Probabilistic Models, Information Retrieval and Graphical Models. Projects are supervised by Computer Science.
• Finance Theme: Pertinent courses are Stochastic Methods in Finance, Forecasting. Projects are supervised by Computer Science or Statistics.
• Natural Computation Theme: Pertinent courses are Evolutionary Systems and Advanced Topics in Machine Learning, which covers visual information processing in natural systems. Students may be supervised by the Gatsby Computational Neuroscience Unit.

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

John Shawe-Taylor
John is the 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 sister course 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

Christian Hennig
Christian is a lecturer in the Statistics Department. His main areas of interest are multivariate analysis (especially clustering and classification), robust statistics, model selection, simulation and bootstrap, explorative and graphical data analysis, philosophical background of statistics and didactics.
Christian'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

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

The following lecturers teach on the Statistics courses

Jinghao Xue

James Nelson

Thomas Fearn

Ricardo Silva

Richard Chandler

Christian Hennig

Dieter Girmes

Julian Herbert

’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 CSML and MSc Machine Learning) 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.’

’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:

* MSc Machine Learning
* MSc Computational Statistics and Machine Learning

 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

David Barber, Programme Director - for Academic queries
Email: d.barber (at) cs.ucl.ac.uk
Phone: +44 (0)20 7679 4151
Fax: +44 (0)20 7387 1397