Key information

Duration: 1 year full-time
Start date: October 2018
Location: South Kensington
ECTS: 90 credit
2019 entry

This course is now closed to applications for 2018 entry.


This specialist course focuses on artificial intelligence and knowledge engineering, as well as the development of computational and engineering models of complex cognitive and social behaviours.

This taught postgraduate course is aimed at students who may not have studied computing exclusively, but who have studied a considerable amount of computing already.

If you want to become a specialist in a particular area of computing, this course will provide a first crucial step towards that goal.

This specialism focuses on artificial intelligence and knowledge engineering, and the development of computational and engineering models of complex cognitive and social behaviours. Study areas include: cognitive robotics, complexity, complex systems, computational finance, computer networks, and distributed systems. We also offer specialisms in:

Each specialism has a flexible mix of breadth and depth, consisting of two or three compulsory modules as well as choices from a selection of core and optional modules.

Professional accreditation

Our MSc Computing Master's degrees are professionally accredited by the IET (Institute of Engineering and Technology).

The accreditation agreement with the IET is due to be renewed for students beginning these degrees in the 2023–24 academic year.


Modules shown are for the current academic year, and are subject to change depending on your year of entry.


Core modules

You take all of the core modules below.

MSc Computing Science (Specialist) Individual Project (Summer)

Prolog (Autumn)

Introduces declarative relational programming using the logic based programming language, Prolog. Focus is on writing small Prolog applications an artificial intelligence dimension.

Short Introduction to Prolog (Autumn)

Introduces the concept of logic programming and syntax and procedural reading of the Prolog language. Teaches the ability to write simple programs to query Prolog databases, and recursively process lists and other compound data structures.

Optional modules – Group 1

You choose three or four modules from below.

Advanced Robotics (Spring)

Addresses topics of advanced robotics, with a focus on real-time state estimation and mapping, with application to drones and Augmented and Virtual Reality.

Advanced Statistical Machine Learning and Pattern Recognition (Spring)

Provides the theoretical and computational skills to understand, design and implement modern statistical machine learning methodologies regarding statistical component analysis, statistical linear dynamical systems and other statistical models.

Computer Vision (Autumn)

Introduces the concepts behind computer-based recognition and extraction of features from raster images.

Data Analysis and Probabilistic Inference (Spring)

Aims to teach how probability can be used to make decisions by a computer. Inference networks form a major part of the material along with linear and non-linear methods in statistical pattern recognition.

Dynamical Systems and Deep Learning (Autumn)

Introduces Deep Belief Nets and Convolutional Neural Nets which provide the two main tools in Deep Learning.

Machine Learning (Spring)

Provides the foundations to Machine Learning (ML) and an understanding of basic ML concepts and techniques. Uses Matlab to design, implement and test ML systems.

Mathematics for Machine Learning (Autumn)

Provides the necessary mathematical background and skills to understand, design and implement modern statistical machine learning methodologies, and inference mechanisms.

Modal Logic* (Autumn)

Develops skills in modal logics for specification, knowledge representation and practical reasoning in artificial intelligence and software engineering.

Reinforcement Learning* (Autumn)

Covers the foundations and standard methods of reinforcement learning. Reinforcement Learning (RL) is a growing sub-area of machine learning concerned with how an agent (computer, human or robot) should choose its actions in an environment so as to maximise some notion of (long-term) reward.

Robotics (Autumn)

Focuses on the field of mobile robotics both theoretically and practically. Covers wheeled locomotion, control, outward-looking sensors, mapping, place recognition and reactive behaviours.

Courses marked * are half courses, and 2 half courses is equal to 1 full course.

Optional modules – Group 2

You choose two to four modules from below.

Argumentation and Multi-agent Systems (Spring)

Focuses on the foundations and advances in Multi-Agent Systems, specifically the concepts and implementation techniques required.

Knowledge Representation (Autumn)

Presents the theoretical foundations for the main logic-based formalisms used for knowledge representation and reasoning in AI, particularly non-monotonic logics and consequence relations, and the computational basis of logic programming.

Logic-Based Learning (Spring)

Gives a foundation of knowledge and basic principles of logic-based learning, to develop basic skills in algorithms and heuristics, and to form a logic-based learning task to solve a given learning problem.

Probabilistic Model Checking and Analysis (Spring)

Systems Verification (Spring)

Introduces formal methods for system specification and verification. Particular prominence is given to logic-based formalisms and techniques, notably model checking.

Optional modules – Group 3

You choose up to three modules from below.

Advanced Computer Architecture (Spring)

Develops a thorough understanding of high-performance and energy-efficient computer architecture, as a basis for informed software performance engineering and as a foundation for advanced work in computer architecture, compiler design, operating systems and parallel processing.

Advanced Computer Graphics (Spring)

Introduces modern techniques in realistic computer graphics and image synthesis, particularly image-based techniques for photorealism.

Advanced Databases (Autumn)

Provides detailed theoretical and practical knowledge of how database management systems (DBMS) are programmed in SQL, how DBMSs may be linked to form distributed databases, and how DBMSs operate and are tuned to improve performance.

Advanced Issues in Object Oriented Programming (Autumn)

Discusses issues around the design and implementation of object oriented languages, the rationale and explore alternatives.

Advanced Security* (Autumn)

Develops an advanced understanding of security topics from both a practical industrially-focused perspective, whilst also providing a storing research perspective.

Complexity (Autumn)

Describes the complexity classes associated with computational problems, and the ability to fit a particular problem into a class of related problems, and so to appreciate the efficiency attainable by algorithms to solve the particular problem.

Computational Finance (Spring)

Introduces the basic concepts of quantitative finance and financial engineering, including hedging and pricing problems in finance, and how to formulate these problems as mathematical models, and understand the computational techniques to solve the arising models.

Computational Optimisation (Autumn)

Develops a deep understanding of optimal decision making models, algorithms and applications to engineering, finance, and machine learning.

Cryptography Engineering (Spring)

Teaches how cryptographic techniques can be used to design and implement secure communicating systems for a variety of different needs and applications, and to do so by considering all aspects from theory to more practical issues.

Custom Computing (Spring)

Custom computers are special-purpose systems customised for specific applications such as signal processing and database operations, when general-purpose computers are too slow, bulky or power hungry. Development of custom computers is an expensive, time-consuming and error-prone activity. This module introduces approaches enabling the rapid and systematic design of custom computers.

Distributed Algorithms (Spring)

Covers key concepts, problems and results in distributed algorithms. Providing an introduction on how to reason about the correctness of distributed algorithms and practical experience of programming them.

Graphics (Spring)

Provides an understanding of basic concepts of computer graphics, and introduces the fundamental mathematical principles used for computer generated imagery, shading and light approximations.

Independent Study Option (Spring)

Study an advanced computer science topic of your choice, ideal for those considering a PhD or a career in industrial research.

Information and Coding Theory (Autumn)

Provides an advanced introduction to information and coding theory which is essential to computer security (e.g. differential privacy, side channel attacks, etc.).

Network and Web Security (Spring)

Covers network and web security broadly from the network to the application layer. The emphasis of the module is on the underlying principles and techniques, with examples of how they are applied in practice.

Operations Research (Autumn)

Studies quantitative methods for decision making, and the emphasis is on numerical algorithms to solve constrained optimisation programs. The methods studied are applicable to problems in many areas: computer science, economics, logistics, and industrial engineering.

Pervasive Computing (Spring)

Pervasive, or Ubiquitous Computing, is the result of technology advancing at exponential rates, enabling computing devices to become smaller, more powerful and more connected.

Principles of Decentralized Ledgers* (Spring)

Decentralised ledgers (such as Bitcoin and Ethereum) have gained rapid popularity, attracting the attention of academics, entrepreneurs, economists, and policy-makers. They promise and already create new disruptive markets, and revolutionize how we think of money and financial infrastructure.

Quantum Computing (Autumn)

Introduces the basic notions of quantum computing with particular emphasis on quantum algorithms.

Simulation and Modelling (Autumn)

Introduces system performance analysis and prediction using computer simulation and mathematical techniques (Markov Processes and queuing theory).

Type Systems for Programming Languages (Autumn)

Courses marked * are half courses, and 2 half courses is equal to 1 full course.

Entry requirements

We welcome students from all over the world and consider all applicants on an individual basis.


Minimum academic requirement

Our minimum requirement is at least a 2.1 UK Honour's degree in science, engineering or computing.

Admissions test (Graduate Record Examination)

You must provide Graduate Record Examination (GRE) scores for Quantitative Reasoning and Verbal Reasoning.

As well as entering the scores on the application form, you must ask the GRE organisation to send validating certificates to the Department.

While there is no minimum requirement for GRE scores, a strong application would include scores higher than 159 for Quantitative Reasoning and higher than 145 for Verbal Reasoning.

If sending via the online system please use 3007 Imperial College as the Institution Code and 0402 Computer Science as the Department Code.

If sending certificates by post, please address them to:

Mr Sam Hesketh
Department of Computing
180 Queen's Gate
London SW7 2AZ

Only the first set of GRE scores received will be considered, and we will not be able to assess an application until we have received the official certificate/online confirmation from the GRE organisation.

Find out more about the GRE.

International qualifications

The academic requirement above is for applicants who hold or who are working towards a UK qualification.

We also accept a wide variety of international qualifications. For guidance see our Country Index though please note that the standards listed here are the minimum for entry to the College.

If you have any questions about admissions and the standard required for the qualification you hold or are currently studying then please contact the relevant admissions team.

English language requirement (all applicants)

All candidates must demonstrate a minimum level of English language proficiency for admission to the College.

For admission to this course, you must achieve the standard College requirement in the appropriate English language qualification. For details of the minimum grades required to achieve this requirement, please see the English language requirements for postgraduate applicants.

Tuition fees and funding

The level of tuition fees you pay is based on your fee status, which we assess based on UK government legislation.

For more information on the funding opportunities that are available, please visit our Fees and Funding website.

Tuition fees

Tuition fees (Home and EU students)

2018 entry

Fees are charged by year of entry to the College and not year of study.

Except where otherwise indicated, the fees for students on courses lasting more than one year will increase annually by an amount linked to inflation, including for part-time students on modular programmes. The measure of inflation used will be the Retail Price Index (RPI) value in the April of the calendar year in which the academic session starts e.g. the RPI value in April 2019 will apply to fees for the academic year 2019–2020.

Tuition fees (Overseas and Islands students)

2018 entry

Fees are charged by year of entry to the College and not year of study.

Except where otherwise indicated, the fees for students on courses lasting more than one year will increase annually by an amount linked to inflation, including for part-time students on modular programmes. The measure of inflation used will be the Retail Price Index (RPI) value in the April of the calendar year in which the academic session starts e.g. the RPI value in April 2019 will apply to fees for the academic year 2019–2020.

Postgraduate Master's loan

If you are a Home or EU student who meets certain criteria, you may be able to apply for a Postgraduate Master’s Loan of up to £10,280 from the UK government. The loan is not means-tested, and you can choose whether to put it towards your tuition fees or living costs.


We offer a range of scholarships for postgraduate students to support you through your studies. Try our scholarships search tool to see what you might be eligible for.

There are a number of external organisations also offer awards for Imperial students, find out more about non-Imperial scholarships.

Accommodation and living costs

Living costs, including accommodation, are not included in your tuition fees.

You can compare costs across our different accommodation options on our Accommodation website.

A rough guide to what you might expect to spend to live in reasonable comfort in London is available on our Fees and Funding website.

How to apply

All applications for postgraduate taught degrees are made online via Apply.Imperial.

You can submit one application form per year of entry, and usually choose up to two courses.

How to apply

Making an application

All applicants must apply online.

You can usually apply for up to two courses, although your second choice will only be considered if your first-choice application is unsuccessful.

Most courses don't have a formal closing date, but popular courses close when they are full, so you should apply early to avoid disappointment. There may also be funding deadlines that apply to you.

You will need to upload documents with your applications, which may include transcripts and degree certificates.

Offer holders will need to pay a deposit to secure your place. This will be deducted from the balance of your tuition fees.

For full details on the online application process, or to start your application, please visit the How to Apply section of our website.

ATAS certificate

An ATAS certificate is not required for overseas students applying for this course.

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