Menu

Taught degree

Machine Learning MSc

The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study machine learning. 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.

Key Information

Modes and duration

  • Full-time: 1 year

Tuition Fees (2015/16)

UK/EU:
£10,765 (FT)
Overseas:
£22,350 (FT)

Application deadlines

All applicants:
1 August 2015
Applications received after the end of March are less likely to be successful.

Entry Requirements

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 and will be expected to have successfully completed as part of their first degree, one or more introductory courses covering appropriate foundation material on machine learning, or to have gained this through industrial experience.

English language requirement: Standard

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.

International equivalencies

International applicants can find out the equivalent qualification for their country by selecting from the list below.

Select your country:

Degree Information

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 three core modules (45 credits), five optional modules (75 credits) and a research project (60 credits).

Core Modules

  • Either Graphical Models or Probabilistic and Unsupervised Learning
  • Programming and Mathematical Methods for Machine Learning
  • Supervised Learning

Options

  • Computational Modelling for Biomedical Imaging
  • Applied Machine Learning
  • Approximate Inference and Learning in Probabilistic Models
  • Affective Computing and Human-Robot Interaction
  • Inverse Problems in Imaging
  • Advanced Topics in Machine Learning
  • Information Retrieval and Data Mining
  • Bioinformatics
  • Machine Vision
  • Evolutionary and Natural Computation

Dissertation/report

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.

Funding

Brown Family Bursary

Value:
£15,000
Duration:
1 year
Eligibility:
Prospective full-time Master's students within the Faculties of the Built Environment, Engineering Science and Mathematical & Physical Sciences.

Brown Family Bursary

Value:
£15,000
Duration:
1 year
Eligibility:
Prospective full-time Master's students within the Faculties of the Built Environment, Engineering Science and Mathematical & Physical Sciences.

Brown Family Bursary

Value:
£15,000
Duration:
1 year
Eligibility:
Prospective full-time Master's students within the Faculties of the Built Environment, Engineering Science and Mathematical & Physical Sciences.

More scholarships are listed on the scholarships website

Careers

Graduates from this programme have an excellent employment record. Within the computer science alumni network students benefit from deep corporate and academic connections. 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.

The programme has a track record of students pursuing machine learning research degrees in domains as diverse as robotics, music, psychology, bioinformatics and universities as broad as Basel, Cambridge, Edinburgh, Nairobi, Oxford and UCL. Graduates have also found positions with multi-national companies such as BAE Systems and BAE Detica.

Top career destinations for this degree

  • Total Gas and Power, Software Developer, 2012,
  • Visual DNA, Software Engineer, 2012,
  • Minecast, Developer, 2011,
  • Cisco, Software Tester, 2011,
  • Excel Academics, Consultant, 2011,

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. ML graduates have been in high demand for PhD positions across the sciences. There is a considerable shortfall in the number of qualified graduates in this area internationally. In London there are many companies looking to understand their customers better and have therefore hired ML graduates. Similarly ML graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in ML graduates, having hired several recently.

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 top master 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.

Student / staff ratios › 80 staff › 650 taught students › 175 research students

Department: Computer Science

Application and next steps

Applications

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.

Application deadlines

All applicants
2015-08-01
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.

UCL+

  • Register interest in your chosen subjects
  • Receive notice of graduate open days, events and more
Register now

Life at UCL

At UCL we're proud of our pioneering history, our distinguished present and our exciting future. UCL is a great place to be a student.

  • World-leading reputation and connections
  • We attract the best and brightest staff and students
  • Significant funding
Find out more