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.
- Benefit from strong industry ties, with close proximity to ‘England’s Silicon Valley’
- Graduate with a Master's degree leading to excellent graduate employability prospects.
- Tailor your learning with a wide range of engaging optional modules
Core ModulesYear 1
This course is designed to enhance your awareness of the many ethical implications of working with advanced technology. The course recognises that the ethical issues in computing and AI come to the forefront through developments in technology, bringing new responsibility for novel ethical, social, and legal implications of technology almost on a daily basis.
This module focuses on acquiring a deep understanding of foundational AI principles and techniques to model complex real-world problems as well as writing algorithms and problems to solve them. The module will start with an introduction to AI that will define core AI concepts, provide the philosophical foundations of AI and discuss ethical issues in this field. The module will continue by covering intelligent agents and classical search to then move to local search and optimisation algorithms. Finally, adversarial search and constraint satisfaction problems will also be taught. All these topics will be covered both from a theoretical point of view, during the lectures, and from a practical point of view during the labs.
The aim of this module is to explain the fundamental principles and quantitative methods in the design and analysis of computational experiments, notions that are at the core of current research and practice in AI. The theoretical concepts taught will be complemented by code examples, through which the student can gain hands-on experience in the methods taught.
This module covers algorithm-independent machine learning; unsupervised learning and clustering; exploratory data analysis; Bayesian methods; Bayes networks and causality; and applications, such as information retrieval and natural language processing. You will develop skills in data analysis, including data mining and statistics.
In this module you will learn how to use MATLAB (Matrix Laboratory) and WEKA (Waikato Environment for Knowledge Analysis) as tools for machine learning and data mining. For MATLAB, you will develop an understanding of how to input and output data using vectors, arrays and matrics; learn techniques in data visualization, including plots in 2 and 3 dimensions, scatter plots, barplots, and histograms; and learn how to implement concepts from linear algebra and statistics, including probability and matrix decompositions. For WEKA, you will develop an understanding of how to use the software as a tool for training and testing, predicting generalisation performance, and cross-validation; and learn how to implement decision trees, naïve Bayes classifiers, and clustering methods.
This specialist module focuses on acquiring a deep understanding of the principles and techniques that are needed to design and build autonomous intelligent systems (AISs). The module will start with an introduction to AISs and real-world examples of them. It will then cover knowledge representation and engineering techniques based on formal logic. The module will then tackle autonomous decision making techniques, from AI planning to probabilistic reasoning and Markov Decision Processes. Reinforcement learning and techniques for cooperation and coordination between artificial agents will also be taught. All these topics will be discussed both from a theoretical point of view, during the lectures, and from a practical point of view, during the labs.
The aim of this module is to teach the necessary background knowledge and practical techniques - especially deep learning - needed to apply natural language processing to large, real-life text-based projects. A brief survey of computational linguistic theory will include notions of syntax, semantics, and pragmatics. Practical techniques for preparing and pre-processing text will be taught in lab sessions. Typical commercial applications of NLP will be surveyed, with practical examples. Standard NLP techniques covered will include: topic modelling and LDA, and construction of word-embeddings.
You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.
All modules are core
Teaching & assessment
The programme will include modules in
- Artificial Intelligence Principles and Techniques
- Natural Language Processing
- Autonomous Intelligent Systems
- Experimental Design
- Data Analysis
- Programming for Data Analysis
- Machine Learning
- Deep Learning
- Semantic Web
- Visualisation and Exploratory Analysis
Computer Science, Economics, Mathematics, Physics, or other subjects that include a strong element of both mathematics and computing.
Normally we require a UK 2:1 (Honours) or equivalent in relevant subjects but we will consider high 2:2 or relevant work experience. Candidates with professional qualifications in an associated area may be considered. Where a ‘good 2:2’ is considered, we would normally define this as reflecting a profile of 57% or above.
International & EU requirements
English language requirements
All teaching at Royal Holloway is in English. You will therefore need to have good enough written and spoken English to cope with your studies right from the start.
The scores we require
- IELTS: 6.5 overall. No subscore lower than 5.5.
- Pearson Test of English: 61 overall. Writing 54. No subscore lower than 51.
- Trinity College London Integrated Skills in English (ISE): ISE III.
For more information about country-specific entry requirements for your country please see here.
Your future career
Our proximity to the M4 corridor – also known as 'England’s Silicon Valley' – provides excellent networking opportunities with some of the country’s top technology institutions. We bring several companies to our campus throughout the year, both for fairs and for delivering advanced topics seminars, which are an excellent opportunity to learn about what they do and discuss possible placements or jobs.
Our graduates enter into successful careers in academia or in companies or organisations operating in highly competitive areas. In recent years, these have included Amazon, American Express, BGL Group, Bupa, Capita, Centrica, EY, Facebook, Google, Hortonworks, JP Morgan, Microsoft, ONS, PWC, QuintilesIMS, Rolls Royce, Shell, UBS, VMware, Xerox and the Z/Yen Group.
In addition to the support provided by The Careers and Employability Service, the department has a dedicated administrator and an academic who coordinates and oversees placements and job opportunities.
- Strong industry ties help to provide placement and networking opportunities with some of the country’s leading institutions.
- Together with our on-site Careers and Employability Service, we run one-to-one coaching sessions and workshops, helping you to find a placement or job and lead a successful career.
Fees & funding
Home and EU students tuition fee per year*: £11,600
The fee for the Year in Industry will be 20% of the first year fee for that academic year
International students tuition fee per year**: £21,000
The fee for the Year in Industry will be 20% of the first year fee for that academic year
Other essential costs***: There are no single associated costs greater than £50 per item on this course.
Scholarships for international studentsIf you hold the equivalent of a UK First Class undergraduate degree, you will automatically be considered for a £2,000 tuition fee discount. Eligible Indian nationals and students domiciled in India, who pay international fees, will receive a £4,000 tuition fee discount.
* and ** These tuition fees apply to students enrolled on a full-time basis. Students studying on the standard part-time course structure over two years are charged 50% of the full-time applicable fee for each study year. All postgraduate fees are subject to inflationary increases. This means that the overall cost of studying the programme via part-time mode is slightly higher than studying it full-time in one year. Royal Holloway's policy is that any increases in fees will not exceed 5% for continuing students. For further information see tuition fees see our terms and conditions.
Please note that for research programmes, we adopt the minimum fee level recommended by the UK Research Councils for the Home/EU tuition fee. Each year, the fee level is adjusted in line with inflation (currently, the measure used is the Treasury GDP deflator). Fees displayed here are therefore subject to change and are usually confirmed in the spring of the year of entry. For more information on the Research Council Indicative Fee please see the RCUK website.
*** These estimated costs relate to studying this particular degree programme at Royal Holloway. Costs, such as accommodation, food, books and other learning materials and printing, have not been included.