UCL (University College London)

Course Introduction

This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Teaching and learning

The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include: finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Employability

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. CSML 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 CSML 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.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence. Coupled with the internationally renowned Gatsby Computational Neuroscience and the Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas. The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

Duration & Attendance Qualification Tuition fees Fee type
1 year
Full Time
MSc £14,320 Home Fees
1 year
Full Time
MSc £30,400 Overseas Fees
1 year
Full Time
MSc £14,320 European Fees

Qualifications required:

  • Domestic entry requirements: A minimum of an upper second-class UK Bachelor's degree in a highly quantitative subject such as computer science, statistics, mathematics, electrical engineering or the physical sciences, or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Students must be comfortable with undergraduate-level mathematics; in particular it is essential that the candidate will have knowledge of statistics at an intermediate undergraduate level. The candidate should also be proficient in linear algebra and multivariable calculus.
  • International entry requirements: A minimum of an upper second-class UK Bachelor's degree in a highly quantitative subject such as computer science, statistics, mathematics, electrical engineering or the physical sciences, or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Students must be comfortable with undergraduate-level mathematics; in particular it is essential that the candidate will have knowledge of statistics at an intermediate undergraduate level. The candidate should also be proficient in linear algebra and multivariable calculus. Students need to have an overall IELTS grade of 7.0 with a minimum of 6.5 in each of the subtests; TOEFL: Overall score of 100 with 24/30 in reading and writing and 20/30 in speaking and listening; Cambridge Certificate of Proficiency in English: Overall score of 185 with 176 in all subtests; Cambridge Certificate of Advanced English: Overall score of 185 with 176 in all subtests; City & Guilds (Pitman Examination in International ESOL): Pass at Mastery level when achieved with a pass at Mastery level in the Pitman International Spoken test (ISESOL); Pearson Test of English (Academic): 69 overall, with a minimum of 62 in each Communicative Skill; Trinity College London, Integrated Skills in English (Trinity ISE) III: Successful completion of ISE III with ?merit? in all components; UCL Diploma in English for Academic Purposes: Overall mark of 70%, with at least 65% in each of the four subtests. UCL International Pre-Master's Courses: Overall mark of 70% with at least 65% in each of the four elements of the examination. Equivalent to 7.0 in IELTS; UCL Pre-sessional English Courses: Mark of 70%, with at least 65% in each of the subtests.
  • IELTS score: 7
  • TOEFL score: 100