Research in Machine Learning at Royal Holloway started in the 1990’s, at which time V. Vapnik and A. Chervonenkis (the inventors of Support Vector Machines) were both professors here. We have developed both fundamental theory and practical algorithms that have fed into the analytics methods and techniques that are in use today. Current researchers include Alexander Gammerman and Vladimir Vovk – the inventors of conformal predictors theory, a radically new method of estimating the accuracy of each prediction as it is made – and Chris Watkins, originator of reinforcement learning who developed ‘Q-learning’, a work that is fundamental to planning and control. More information is available here.

We also carry out world-leading research in all aspects of distributed computing and systems – including design and analysis of algorithms, large-scale and cloud-based systems, fault-tolerance, distributed storage, cloud computing, peer-to-peer, concurrency control, and multi-core computing – and in artificial intelligence, including cognitive and autonomous agents, automated planning, scheduling and domain-independent search control, and applications in surveillance operations, disaster response missions, space operations.

We work closely with companies such as Facebook, IBM, NASA, GCHQ, Centrica (British Gas, Hive), Cognizant, QinetiQ, Thales UK, Orange Labs (UK), Yahoo! Research, the UK Cards Association, Transport for London and ITSO, among others. Thanks to our multiple connections with companies and organisations, you will also interact with many professionals during your studies, including former Royal Holloway students and post-docs who went on to work for Google, Microsoft, Amazon, and Facebook.

We have state-of-the-art networked computer facilities that can be accessed from off campus using VPN. Students have access to a dedicated computing cluster HP DL380 comprising 32 processors, 32 Terabytes of disk storage, and GPU. The cluster is currently running the full-fledged Hortonworks Hadoop distribution (HDP) as well as the PostgreSQL and MongoDB databases. It also serves as a repository for massive data sets provided by our industrial and academic partners. We are installing a new GPU server with two AMD 64-Core Processors, a terabyte of RAM, and four 80 Gigabyte NVIDIA A100 Tensor Core GPUs.

A Masters degree awards 180 credits (90 ECTS): 120 credits are obtained through modules taught during two terms, and 60 credits are obtained through a project or dissertation supervised by an academic member of staff during 12 weeks over the summer. The total duration is circa 12 months unless you study part time (which allows you to take up to five years) or go on a placement/internship (which is up to one additional year).

Students who complete the taught part but choose not to do (or fail) the project are awarded a postgraduate diploma. 

A postgraduate certificate is awarded to students who complete 60 credits of taught modules.

Our degrees can be taken part-time and modules can be taken in CPD mode. Please check the "Part-time studies" tab below.

Equipped with Artificial Intelligence techniques, today's systems can teach themselves to perform tasks almost as well as humans can. This degree provides you with the foundational knowledge and the practical skills required to operate with these disruptive technologies.

Core modules:

• Artificial Intelligence Principles and Techniques 
• Autonomous Intelligent Systems
• Data Analysis
• Principles of Computation and Programming (unless you have already acquired the required knowledge)
• Ethics in Advanced Computing and Artificial Intelligence

See this page for an outline of all modules, from which you will also choose your electives. The list includes Deep Learning, Natural Language Processing, Online Machine Learning, and other relevant modules. Note that availability may vary from year to year.

For more information, including entry requirements and fees, please follow these links:

• MSc in Artificial Intelligence
• MSc in Artificial Intelligence with a Year in Industry

This degree programme, offered jointly with the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling.

Core modules:

• Data analysis
• Principles of Computation and Programming (unless you have already acquired the required knowledge)
• Foundations of finance
• Database systems (unless if you have already acquired the required knowledge)
• Investment and portfolio management
• Ethics in Advanced Computing and Artificial Intelligence

See this page for an outline of all modules, from which you will also choose your electives. The list includes Business Intelligence, Visualisation and Exploratory Analysis, Fixed Income Securities and Derivatives, Time Series Econometrics, and other relevant modules in Computer Science and Economics. Note that availability may vary from year to year.

For more information, including entry requirements and fees, please follow these links:

• MSc in Computational Finance
• MSc in Computational Finance with a Year in Industry

This degree programme prepares you for a career as a data scientist; you will be taught both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields.

Core modules:

• Data analysis
• Principles of computation and programming (unless if you have already acquired the required knowledge)
• Database systems (unless if you have already acquired the required knowledge)
• Large-scale data storage and processing
• Ethics in Advanced Computing and Artificial Intelligence

See this page for an outline of all modules, from which you will also choose your electives. The list includes Business Intelligence, Visualisation and Exploratory Analysis, Deep Learning, Online Machine Learning and other relevant modules. Note that availability may vary from year to year.

For more information, including entry requirements and fees, please follow these links:

• MSc in Data Science and Analytics
• MSc in Data Science and Analytics with a Year in Industry

This degree programme allows you to develop a deeper understanding of the science of systems that can learn from data, which for example companies such as Facebook, Google, Microsoft and Yahoo require to create, innovate, and define the next generation of search and analysis technologies.

Core modules:

• Data analysis
• Principles of Computation and Programming (unless you have already acquired the required knowledge)
• Machine learning
• Online machine learning
• Deep learning
• Ethics in Advanced Computing and Artificial Intelligence

See this page for an outline of all modules, from which you will also choose your electives. The list includes Natural Language Processing, Visualisation and Exploratory Analysis, Reinforcement Learning, and other relevant modules. Note that availability may vary from year to year.

For more information, including entry requirements and fees, please follow these links:

• MSc in Machine Learning
• MSc in Machine Learning with a Year in Industry

MSc Applied Data Science is a new masters designed for those wanting to learn the skills required to gain insights from data.

The conversion course enables students with little prior exposure to computing to develop expertise and valuable skills that will prepare you for a career in the growing data science industry where there is significant demand for skilled personnel, both in the UK and internationally. (Students with an interest in cyber security may also consider our combined MSc Applied Data Science and Cyber Security).

Please note this course is open for January 2025 entry only.

To read more about this course click here