The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain.
Modes and duration
Tuition fees (2017/18)
- £11,800 (FT)
- £24,610 (FT)
Note on fees: The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Current Students website.
A minimum of an upper second-class UK Bachelor's degree in a highly quantitative subject such as computer science, mathematics, electrical engineering or the physical sciences, or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Additionally, candidates must be comfortable with undergraduate mathematics in areas such as linear algebra and calculus.
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.
International applicants can find out the equivalent qualification for their country by selecting from the list below.
Select your country:
Students develop an understanding of the principles underlying the development and application of new techniques in this area, alongside an awareness of, and ability to analyse the range and scope of algorithms and approaches available, and design, develop and evaluate appropriate algorithms and methods for new problems and applications.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), six optional modules (90 credits) and a research project (60 credits).
- Supervised Learning
- Either Graphical Models or Probabilistic and Unsupervised Learning
- Machine Vision
- Information Retrieval and Data Mining
- Advanced Topics in Machine Learning
- Inverse Problems in Imaging
- Affective Computing and Human-Robot Interaction
- Approximate Inference and Learning in Probabilistic Models
- Applied Machine Learning
- Computational Modelling for Biomedical Imaging
- Programming and Mathematical Methods for Machine Learning
- Statistical Natural Language Programming
- Numerical Optimisation
All MSc students undertake an independent research project which culminates in a dissertation ( maximum length of 120 pages) in the form of a project report.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed though a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Graduates from this programme have an excellent employment record. Substantial sectors of UK industry, including leading, large companies already make extensive use of intelligent systems techniques in the course of their business activities, and the UK has a number of very successful developers and suppliers of the technology. Students also benefit from strong corporate and academic connections within the UCL Computer Science alumni network.
Graduates have machine learning research degrees in domains as diverse as robotics, music, psychology, bioinformatics at the universities of Basel, Cambridge, Edinburgh, Nairobi, Oxford and at UCL. Graduates have also found positions with multi national companies such as BAE Systems and BAE Detica.
Top career destinations for this degree
- Software Engineer, Bisual
- PhD Computer Programming, Newcastle University
- Software Developer, Total Gas & Power
- Risk Analyst, National Bank of Greece
- Research Engineer, Xerox Research Centre India
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. Machine Learning graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.
Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2012–2014 graduating cohorts six months after graduation.
Why study this degree at UCL?
UCL Computer Science is recognised as a world leader in teaching and research, and our Master's programmes have some of the highest employment rates and starting salaries.
We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.
This MSc is one of the few leading Master's programmes entirely dedicated to machine learning. It combines a rigorous theoretical academic framework along with specific knowledge of a variety of application fields to fast-track your commercial career or to prepare for PhD research.
Department: Computer Science
Student / staff numbers › 200 staff including 120 postdocs › 650 taught students › 180 research students
Research Excellence Framework (REF)
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Computer Science
96% rated 4* (world-leading) or 3* (internationally excellent)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
Application and next steps
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Who can apply?
The programme provides a sound basis for those embarking on a career in research or development or taking up positions within industry where machine learning is currently applied or will be applied in the future; such as finance, banking and insurance, retail and web-commerce, pharmaceuticals, computer security and web search.
- All applicants
- 17 June 2017
Applications received after the end of March are less likely to be successful.
For more information see our Applications page.Apply now
What are we looking for?
When we assess your application we would like to learn:
- why you want to study Machine Learning at graduate level
- why you want to study Machine Learning at UCL
- what particularly attracts you to this programme
- how your academic and professional background meets the demands of this programme
- what programming experience you have
- where you would like to go professionally with your degree
Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.
Successful applicants to this programme will be required to pay a tuition fee deposit dependent on their mode of study and fee status as given below:
- UK/EU full-time: £2,000
- Overseas full-time: £2,000
Further details can be found on the Fees and funding page.