Prospective students

The MRes in Computational Statistics and Machine Learning prepares students for a research career in machine learning and related large-scale data analysis. 

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 that have some prior familiarity with data analysis and wish to engage in a substantial research project, prior to continuing a research career.  Taking the MRes will give students a deeper understanding of a research level topic and also enable the department to more fully assess the future research potential of potential PhD applicants.

The MRes is taught jointly by the Department of Computer Science and the Department of Statistical Science.


Upcoming Events and Open Days

Virtual Open Day - 5th April 2017, 1pm - 2pm

 

Find out more about the benefits of studying the MRes Computational Statistics and Machine Learning at UCL, the top-rated university in the UK for research in Computer Science.

Take the opportunity to (virtually) meet the team behind the Masters, with a chance to chat with them via dedicated online forums. 

For further information and to register your attendance, please click here.

Course Structure for 2017/18

The MRes programme is split into core and optional modules and a research project. Students must take 2 core modules and 3 optional modules. They will also complete a research project over a period of 9 months.

Core Modules

EDUCGE01 Investigating Research

EDUCGE01 Investigating Research

This module is taught by UCL Centre for Advancing Learning and Teaching.

 

More information can be found here.

EDUCGE02 Researcher Profesional Development

EDUCGE02 Researcher Profesional Development

This module is taught by UCL Centre for Advancing Learning and Teaching.

 

More information can be found here.

COMPGM98 MRes Dissertation

COMPGM98 MRes Dissertation

A research project over a period of 9 months.

Optional Modules

COMPGI01 Supervised Learning

COMPGI01 Supervised Learning

This module covers supervised approaches to machine learning. It starts by reviewing fundamentals of statistical decision theory and probabilistic pattern recognition followed by an in-depth introduction to various supervised learning algorithms such as Perceptron, Backpropagation algorithm, Decision trees, instance-based learning, support vector machines. Algorithmic-independent principles such as inductive bias, side information, approximation and estimation errors. Assessment of algorithms by jackknife and bootstrap error estimation, improvement of algorithms by voting methods such as boosting. Introduction to statistical learning theory, hypothesis classes, PAC learning model, VC-dimension, growth functions, empirical risk minimization, structural risk minimization.

Students will gain an in-depth familiarity with various classical and contemporary supervised learning algorithms, understand the underlying limitations and principles that govern learning algorithms and ways of assessing and improving their performance, understand the underlying fundamentals of statistical learning theory, the complexity of learning and its relationship to generalization ability.

 

Further syllabus information can be found here.

COMPGI08 Graphical Models

COMPGI08 Graphical Models

The module provides an entry into probabilistic modeling and reasoning, primarily of discrete variable systems. Very little continuous variable calculus is required, and students more familiar with discrete mathematics should find the course digestible. The emphasis is to demonstrate the potential applications of the techniques in plausible real-world scenarios related to information retrieval and analysis. Concrete challenges include questionnaire analysis, low-density parity check error correction, and collaborative filtering of Netflix data.

 

Further syllabus information can be found here.

COMPGI13 Advanced Topics in Machine Learning

COMPGI13 Advanced Topics in Machine Learning

Kernel methods

To gain an understanding of the theory and applications of kernel methods, including:

  • An overview of how kernel feature spaces can be constructed, including in infinite dimensions, and the smoothing properties of functions in these spaces.
  • Simple and complex learning algorithms using kernels (ridge regression, kernel PCA, the support vector machine)
  • Representations of probabilities in reproducing kernel Hilbert spaces. Statistical two-sample and independence tests, and learning algorithms using these embeddings (clustering, ICA)

Learning theory

To learn the fundamentals of statistical learning theory. In particular to:

  • Understand what characterizes a learning problem and what it means for an algorithm/system/machine to “learn”.
  • Understand the key role of regularization and the different approaches to use it efficiently in practice.
  • Acquire familiarity with a variety of statistically consistent learning algorithms, both from modeling and practical perspectives.

Further syllabus information can be found here.

COMPGI14 Machine Vision

COMPGI14 Machine Vision

The course addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.

At the end of the course, students will be able to understand and apply a series of probabilistic models of images and objects in machine vision systems. To understand the principles behind face recognition, segmentation, image parsing, super-resolution, object recognition, tracking and 3D model building.

 

Further syllabus information can be found here.

COMPGI15 Information Retrieval & Data Mining

COMPGI15 Information Retrieval & Data Mining

The course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. Practical retrieval and data mining applications such as web search engines, personalisation and recommender systems, business intelligence, and fraud detection will also be covered.

Students are expected to master both the theoretical and practical aspects of information retrieval and data mining.

 

Further syllabus information can be found here.

COMPGI16 Approximate Inference and Learning in Probabilistic Models

COMPGI16 Approximate Inference and Learning in Probabilistic Models

The module will present the foundations of approximate inference and learning in probabilistic graphical models (e.g. Bayesian networks and Markov networks), with particular focus on models composed from conditional exponential family distributions. Both stochastic (Monte Carlo) methods and deterministic approximations will be covered. The methods will be discussed in relation to practical problems in real-world inference in Machine Learning, including problems in tracking and learning.

 

Further syllabus information can be found here.

COMPGI18 Probabilistic & Unsupervised Learning

COMPGI18 Probabilistic & Unsupervised Learning

This module provides students with an in-depth introduction to statistical modelling and unsupervised learning techniques. It presents probabilistic approaches to modelling and their relation to coding theory and Bayesian statistics. A variety of latent variable models will be covered including mixture models (used for clustering), dimensionality reduction methods, time series models such as hidden Markov models which are used in speech recognition and bioinformatics, independent components analysis, hierarchical models, and nonlinear models.

 

Further syllabus information can be found here.

COMPGI21 Introduction to Machine Learning

COMPGI21 Introduction to Machine Learning

Students will become familiar with the conceptual landscape of machine learning and have developed practical skills to solve real world problems using available software.

 

Further syllabus information can be found here.

COMPGI22 Advanced Deep Learning and Reinforcement Learning

COMPGI22 Advanced Deep Learning and Reinforcement Learning

Students successfully completing the module should understand:

  1. The basics of deep learning and reinforcement learning paradigms.
  2. Architectures and optimization methods for deep neural network training.
  3. How to implement deep learning methods within a given ML framework and apply them to data.
  4. The theoretical foundations and algorithms of reinforcement learning.
  5. How to apply reinforcement learning algorithms to environments with complex dynamics.

 

Further syllabus information can be found here.

COMPGI23 Introduction to Deep learning

COMPGI23 Introduction to Deep learning

At the conclusion of this module students should understand: 

 

  1. The fundamental principles, theory and approaches for learning with deep neural networks
  2. The main variants of deep learning (such convolutional and recurrent architectures), and their typical applications
  3. The key concepts, issues and practices when training and modeling with deep architectures; as well as have hands-on experience in using deep learning frameworks for this purpose
  4. How to implement basic versions of some of the core deep network algorithms (such as backpropagation)
  5. How deep learning fits within the context of other ML approaches and what learning tasks it is considered to be suited and not well suited to perform

 

Further syllabus information can be found here.

COMPGV08 Inverse Problems in Imaging

COMPGV08 Inverse Problems in Imaging

To introduce the concepts of optimisation, and appropriate mathematical and numerical tools applications in image processing and image reconstruction.

 

Further syllabus information can be found here.

STATG001 Statistical Models & Data Analysis

STATG001 Statistical Models & Data Analysis

Taught by Statistics - see here for syllabus.

STATG003 Statistical Computing

STATG003 Statistical Computing

Taught by Statistics - see here for syllabus

STATG004 Applied Bayesian Methods

STATG004 Applied Bayesian Methods

Taught by Department of Statistical Science - see here for syllabus

STATG012 Statistical Inference

STATG012 Statistical Inference

Taught by Statistics - see here for syllabus

STATG019 Selected Topics in Statistics

STATG019 Selected Topics in Statistics

Taught by Statistics - see here for syllabus

You will need to choose 45 credits from the optional modules.

  • The MRes is designed for people who have a first or upper second class honours BSc (or equivalent) in a numerate discipline such as Computer Science, Mathematics, Engineering or the Physical Sciences. 
  • As the course is co-taught with the Department of Statistical Sciences, we require candidates to have studied a significant mathematics and/or statistics component as part of their degree, to ensure they are able to cope with the level of Statistics involved.
  • Students should also have some experience with a programming language such as Matlab.
  • Appropriate industrial experience may also be considered in some cases

 

English Language Requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.

The English language level for this programme is: Good

Further information can be found on our English language requirements page.

 

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

UK/EU fees (FT):  £11,800 for 2017/18

Overseas fees (FT):  £25,130 for 2017/18

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarship and Funding website.

Tuition Fee Deposit

This programme requires that applicants firmly accepting their offer pay a deposit. This allows UCL to effectively plan student numbers, as students are more demonstrably committed towards commencing their studies with us.

For full details about the UCL tuition fee deposit, please see the central UCL pages.

Tuition fee deposits within the Department of Computer Science are currently listed as:

UK/EUOverseas
Full-time*Part-timeFull-time*Part-time
£2000£1000£2000£1000
*where part-time is an available mode of study

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Top graduate roles:                          

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Further study destinations:

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Average starting salary £47,500 (Graduate Surveys, January 2015).

Programme Administrator
Rebecca Martin
Office 5.22, Malet Place Engineering Building 
+44 (0)20 7679 0481
advancedmsc-admissions@cs.ucl.ac.uk

More information

Before you apply:

The MRes CSML is classed as a research degree and you are required to submit a research proposal and identify a potential supervisor. Further information is available here.
We will not be able to progress your application until we have received your research proposal and suggestion of a suitable supervisor.

Please note that you will also be required to submit the details of 3 referees.

To apply now click here.

Students are advised to apply as early as possible due to competition for places, later applications may be less successful. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Deadline 17th June 2017