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.
The MRes programme is split into core and optional modules and a research project. Students must take 1 core modules and 4 optional modules. They will also complete a research project over a period of 9 months.
Compulsory / Core Modules
- COMP0097 - MRes Computational Statistics and Machine Learning Dissertation (105 credits)
- EDUC0001 - Investigating Research (15 credits)
All modules in this group are compulsory.
- COMP0089 - Advanced Deep Learning and Reinforcement Learning (15 credits)
- COMP0083 - Advanced Topics in Machine Learning (15 credits)
- COMP0085 - Approximate Inference and Learning in Probabilistic Models (15 credits)
- COMP0080 - Graphical Models (15 credits)
- COMP0084 - Information Retrieval and Data Mining (15 credits)
- COMP0090 - Introduction to Deep Learning (15 credits)
- COMP0088 - Introduction to Machine Learning (15 credits)
- COMP0114 - Inverse Problems in Imaging (15 credits)
- COMP0137 - Machine Vision (15 credits)
- COMP0086 - Probabilistic and Unsupervised Learning (15 credits)
- COMP0078 - Supervised Learning (15 credits)
- EDUC0002 - Researcher Professional Development (15 credits)
- STAT0031 - Applied Bayesian Methods (15 credits)
- STAT0017 - Selected Topics in Statistics (15 credits)
- STAT0030 - Statistical Computing (15 credits)
- STAT0008 - Statistical Inference (15 credits)
- STAT0028 - Statistical Models and Data Analysis (15 credits)
Choose 60 credits from these optional modules.
All choices are subject to timetabling constraints and the approval of the relevant Module Tutors (i.e. to ensure any prerequisites are satisfied) and the Programme Director.
Syllabus content for all postgraduate modules can we found in the Department of Computer Science 2018/19 online syllabus pages.
Programme diet (modules available to you)
Your programme has a set curriculum (also called a diet) which prescribes in what combinations modules can be taken, any restrictions on doing so, and how much credit can and must be taken. The programme information pages show which modules form part of each programme, with links to descriptions and module syllabus information. Modules within a programme can be core, optional, or elective, which reflects whether they must be taken or are optionally taken.
Core modules are fundamental to your programme’s core curriculum and are mandatory. You will automatically be registered on your programme's core modules, so will not have to select them. You are guaranteed a place on modules that are core for your programme. There will be no timetable clashes between core modules within a programme.
Optional modules are usually closely related to the programme's core curriculum and you will be able to choose which to take; choices are usually made from within specific groups (for example, choose two optional modules from one group and three from another, etc.) You are not guaranteed a place on optional modules as space is strictly limited. We allocate places on a first come, first serve basis, with preference given to Computer Science students over those of other departments. Bear in mind that some modules have prerequisites that must be met in order to be eligible for a place (see the module syllabus for information.)
Elective modules are usually not specifically related to the programme's curriculum. There is no guarantee of being accepted onto an elective module; they are core and/ or optional on other programme diets, so students on those programmes will be given priority. As with optional modules, some electives have prerequisites that must be met.
Deciding which modules to select
The programme information pages show which modules form part of each programme, with links to detailed module syllabus information and reading lists. You may be able to virtually audit lectures for some modules to get a sense of how the module is delivered. You can look up the timetable for each module via the common timetable to get a sense of the timetable that would eventuate from your module choices, which is an important consideration when making your final choices; you should aim to achieve a timetable that is feasible and will not stretch you too thinly.
Bear in mind that places on optional and elective modules are not guaranteed, so you might not always be able to take all your first choices. In that case, it is a good idea to have a second preference in mind.
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.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
2019/20 Tuition Fees
UK/EU Fees (FT):
£13340 for 2019/20
UK/EU Fees (PT):
N/A for 2019/20
Overseas Fees (FT):
£28410 for 2019/20
Overseas Fees (PT):
N/A for 2019/20
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:
|*only applicable where part-time is an available mode of study|
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Average starting salary £47,500 (Graduate Surveys, January 2015).
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.
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 14th June 2019.