Postgraduate MSc
Computer Science (Artificial Intelligence) with an Industrial Placement
Accreditation
This is a conversion course intended for graduates with little or no prior knowledge of computer science. For those with prior knowledge of computer science, see our specialist MSc Artificial Intelligence course.
This conversion course has been designed to provide graduates from any discipline with an introduction to computer science and artificial intelligence to develop skills to restart your career, get into technology and succeed within an evolving industry.
The Computer Science (Artificial Intelligence) course prepares graduates for a career in computing, or a career involving the application of computing and artificial intelligence for problem solving in any professional field. Working with the latest technologies, you develop skills which will boost your career prospects as computer science is used for problem solving in every industry. You can explore a wide range of personal interests and career ambitions through a wide range of optional modules.
Graduates from our computer science conversion courses have successfully achieved careers with leading software, technology, and commercial global companies such as IBM, Cisco, Logica/CMG, Pfizer, Reuters, Shell and Zurich Financial.
This course includes an industrial placement of between eight and 50 weeks. This provides an opportunity to work in real-world, technical and business roles, enhancing your study experience and having a dramatic impact on your choices after graduation. Our dedicated placement team can help you gain a suitable paid position and provide support throughout.
This degree has been partially accredited by BCS, The Chartered Institute for IT.
Our world-leading researchers, in key areas such as cyber security, programming languages, computational intelligence and data science, earned us an outstanding result in the recent Research Excellence Framework (REF). Our submission was ranked 12th in the UK for research intensity, with an impressive 98% of our research judged to be of international quality.
Strong links with industry underpin all our work, notably with Cisco Systems Inc, Microsoft, Oracle, IBM, Nvidia, Erlang Solutions, GCHQ and Google.
Our programmes are taught by leading researchers who are experts in their fields. The School of Computing at Kent is home to several authors of leading computer science textbooks. Kent was awarded gold, the highest rating, in the UK Government’s Teaching Excellence Framework*.
While studying with us, you can gain work experience through an industrial placement. Our dedicated placement team can help you gain a suitable paid position and provide support throughout your placement.
We have a large range of equipment providing both Linux and PC-based systems. Our resources include a multicore enterprise server and a virtual machine server that supports computer security experiments.
The School also has a makerspace, The Shed, which offers exciting teaching and collaboration opportunities. Among other equipment it contains a milling machine, 3D printers, laser cutter and extensive space for building and making digital artefacts.
Computers are good at mechanical tasks but can they be creative? In this talk, Dr Anna Jordanous looks at why we would want to study computers being creative and what we can learn from this work.
*The University of Kent's Statement of Findings can be found here
All applicants are considered on an individual basis and additional qualifications, professional qualifications and relevant experience may also be taken into account when considering applications.
Please see our International Student website for entry requirements by country and other relevant information. Due to visa restrictions, students who require a student visa to study cannot study part-time unless undertaking a distance or blended-learning programme with no on-campus provision.
The University requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree. Certain subjects require a higher level.
For detailed information see our English language requirements web pages.
Please note that if you are required to meet an English language condition, we offer a number of pre-sessional courses in English for Academic Purposes through Kent International Pathways.
The following modules are indicative of those offered on this programme. This list is based on the current curriculum and may change year to year in response to new curriculum developments and innovation. Most programmes will require you to study a combination of compulsory and optional modules.
Students are presented during lectures with advanced Artificial Intelligence/Machine Learning techniques (such as genetic algorithms, support vector machines (SVMs), deep learning, neural networks, stochastic gradient decent, Q-Learning/Deep Q-learning, ensembles, neuroevolution), including aspects of implementation, hyper parameter tuning, scalability and parallelism.
This module covers the design and implementation of high-quality software, and provides an introduction to software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including effective graphical representations.
Throughout the module, students will learn to embed data analysis and statistics concepts into a programming language which offers good support for AI (e.g., Python). Students will learn to use important AI-purposed libraries and tools, and apply these techniques to data loading, processing, manipulation and visualisation.
This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a data mining tool, and learn to evaluate the quality of discovered knowledge.
There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. Similarly, there is now also an increasing interest in understanding how biological, chemical and other natural systems compute, and how this could be exploited for practical applications. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their later careers.
Data types: nominal, numerical, ordinal, text, audio, visual, temporal and non-temporal. Basic descriptive statistics: measures of average and spread, different ways of graphing data. Choosing appropriate and valid methods for the analysis and presentation of data, and understanding the limitations of methods. Data at different scales, including big data, and the computational challenges of processing data at scale. The process of discovering useful knowledge from data: including understanding the need for preprocessing and cleaning data, the challenges of gathering relevant data, and the need to present results in a comprehensible and actionable way. Data mining: classification/regression and clustering, and the idea of predictive analytics. Elements of information retrieval from text. Vector representations of text documents. Fairness and ethical issues concerning data.
This module covers the fundamental components (hardware and software) of a typical computer system, and how they collaborate to execute programs. The module provides a comprehensive overview, from the lowest level of abstractions in hardware to the highest level of abstractions of modern programming languages. Examples of topics that may be covered include logic circuits, machine language, processor organization, memory management, processes and concurrency, file systems. Throughout, special attention is paid to abstraction, performance, and other quality requirements.
This module provides an introduction to human-computer interaction, user experience and a range of UX practices (UX - user experience - the study and practice of how people, individually and in groups, experience technologies and other artefacts, and interact with and through them.)
Fundamental aspects of human physiology and psychology are introduced and key features of interaction and common interaction styles delineated. A variety of design methods and UX practices are introduced (e.g. task-based usability testing, think-aloud protocols, first-use experiences, eye-tracking and post-session questionnaires). Throughout the course, the need for a professional, integrated and user-centred approach to interface development and evaluation is emphasised: rapid and low-fidelity prototyping feature as one aspect of this.
The module aim is to give students an overview and understanding of key theoretical, practical and philosophical research and issues around computational creativity, and to give them practical experience in writing and evaluating creative software.
The following is an indicative list of topics that may be covered:
• Introduction to computational creativity
• Examples of computational creativity software e.g. musical systems, artistic systems, linguistic systems, proof generator systems, systems for 2D and 3D design.
• Evaluation of computational creativity systems (both of the quality and the creativity of systems)
• Philosophical issues concerning creativity in computers
• Comparison of computer creativity to human creativity
• Collaborative creativity between humans and computers
• Overview of recent research directions/results in computational creativity
• Practical experience in writing creative software.
This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. Students will learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.
In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also familiarise yourself with cognitive neural networks using state of the art simulation technology and apply these networks to the solution of problems. In addition, the module discusses examples of computation applied to neurobiology and cognitive psychology. The module also introduces artificial neural networks from the machine learning perspective. You will study the existing machine learning implementations of neural networks, and you will also engage in implementation of algorithms and procedures relevant to neural networks.
This module looks into the training of modern deep neural networks: backpropagation, regularisation, automatic differentiation, computational graphs. Introduces different types of deep neural networks, such as, LSTM, convolutional networks, and autoencoders. Presents the theoretical underpinnings of deep learning and its mechanisms. Delves into selected recent advanced topics in deep learning. Examines applications of deep learning.
This module provides an introduction to object-oriented programming using the popular Java language. It is designed for beginners who have not studied computer programming before. By the end students will be able to develop simple programmes using Java.
This module aims to strengthen the foundational programming-in-the-small abilities of students via a strong, practical problem-solving focus. Specific topics will include introductory algorithms, algorithm correctness, and algorithm runtime. Essential data structures and algorithmic programming skills will be covered, for example, arrays, lists and trees, searching and sorting, recursion, and divide and conquer. One part of the module will also introduce students to logical formalisms such as propositional and predicate logic and some of their applications in program development.
• Software processes.
• Modelling techniques, and the use of these techniques throughout the project lifecycle.
• Introduction to modelling principles (decomposition, abstraction, generalization,
projection/views) and types of models (information, behavioural, structural, domain and
functional).
• Risk and risk management in software.
• Approaches to software testing and inspection.
• Approaches to software configuration management.
• Security and privacy in software engineering
• Software engineering tools: configuration control, project management, integrated
development environments and modelling tools.
Web-based information systems form the heart of e-commerce. They are also increasingly the way businesses handle all their information needs. Building such systems requires an understanding of up-to-date tools and technologies such as web page creation, client side programing, server side programming and databases; it also calls for an understanding of how to design systems that genuinely meet user and business needs.
The focus of the module is on the development of the advanced English language competence necessary for post graduate study in scientific disciplines. This includes the ability to interpret and evaluate authentic scientific texts; analyse, discuss and summarise written and visual information both in writing and orally; organise written texts effectively and submit them in grammatically accurate English, and present the results of research orally in a coherent and stimulating way.
The project consists primarily of an extended period during which students undertake a substantial piece of work and a report on this in the form of a dissertation. It is preceded by an exploratory stage in which students review and summarise relevant literature or other technical background, including in a verbal presentation, and gain specific skills relevant to their project. It may be permitted to undertake the work in groups, particularly for projects with a development focus. However, the dissertations are produced individually. The project examines the student's ability to research technical background, to understand and expand on a specific problem commensurate with their programme of study and relate it to other work, to carry out investigations and development (as appropriate), to describe results and draw conclusions from them, and to write a coherent and well organised dissertation demonstrating the student's individual reflection and achieved learning.
Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the Employability and Placements Team. This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.
Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the Employability and Placements Team. It is also dependent on satisfactory achievement in their academic studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.
Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.
Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.
Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.
Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.
Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.
Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.
Students spend a period (minimum 36 weeks and maximum 44 weeks) working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the Employability and Placements Team. This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.
Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.
Duration: 1 year full-time
Each of our taught MSc courses is available in several formats to accommodate students from different backgrounds and to provide maximum flexibility. See more about Taught Master's course formats.
Assessment is through a mixture of written examinations and coursework, the relative weights of which vary according to the nature of the module. The final project is assessed by a dissertation.
The School of Computing has a large range of equipment providing both UNIX (TM) and PC-based systems and a cluster facility consisting of 30 Linux-based PCs for parallel computation. New resources include a multi-core enterprise server with 128 hardware threads and a virtual machine server that supports computer security experiments.
All students benefit from a well-stocked library, giving access to e-books and online journals as well as books, and a high bandwidth internet gateway. The School and its research groups hold a series of regular seminars presented by staff as well as by visiting speakers and our students are welcome to attend.
The School of Computing has a makerspace which offers exciting new teaching and collaboration opportunities. Among other equipment, it contains milling machines, a 3D printer, laser cutter and extensive space for building and making digital artefacts.
Our taught postgraduate students enjoy a high level of access to academic staff and have their own dedicated laboratory and study room. Students whose course includes an industrial placement are supported by a dedicated team which helps them gain a suitable position and provides support throughout the placement.
Staff and research students publish regularly and widely in journals, conference proceedings and books. Among others, they have recently contributed to: Journal of Artificial Evolution and Applications; International Journal of Computer and Telecommunications Networking; Journal of Visual Languages and Computing; Journal in Computer Virology.
Strong links with industry underpin all our work, notably with Cisco, Microsoft, Oracle, IBM, Agilent Technologies, Erlang Solutions, Hewlett Packard Laboratories, Ericsson and Nexor.
All students registered for a taught Master's programme are eligible to apply for a place on our Global Skills Award Programme. The programme is designed to broaden your understanding of global issues and current affairs as well as to develop personal skills which will enhance your employability.
This Group brings together interdisciplinary researchers investigating the interface between computer science and the domains of bioscience and cognition. In terms of applying computation to other domains, we have experts in investigating the modelling of gene expression and modelling of human attention, emotions and reasoning. From the perspective of applying biological metaphors to computation, we research new computational methods such as genetic algorithms and swarm intelligence.
The Group also develops novel techniques for data mining, visualisation and simulation. These use the results of interdisciplinary research for finding solutions to computationally expensive problems.
The Group has strong links with other schools at the University of Kent, as well as with universities, hospitals and scientific research institutes throughout the country and internationally.
Areas of research activity within the group include:
Our research involves all aspects of programming languages and systems, from fundamental theory to practical implementation. The Group has interests across a wide range of programming paradigms: object-oriented, concurrent, functional and logic. We research the links between logic and programming languages, the verification of the correctness of programs, and develop tools for refactoring, tracing and testing. We are interested in incorporating safe concurrent programming practices into language design.
The Group is also interested in practical implementation of programming languages, from massively concurrent parallel processing to battery-operated mobile systems. Particular research topics include lightweight multi-threading kernels, highly concurrent operating systems, memory managers and garbage collectors.
Research areas include:
Security - of information, systems, and communications - has become a central issue in our society. Interaction between people's personal devices (far beyond just phones and computers) and the rest of the connected world is nearly continuous; and with the advent of the Internet Of Things its scope will only grow.
In that context, so much can go wrong - every communication can potentially be intercepted, modified, or spoofed, and surreptitiously obtained data can be commercially exploited or used for privacy invasions. In fact, data flows in society are such that many people already feel they have lost control over where (their) data goes.
The cyber security research group operates within that context. All members bring a particular technological emphasis - the analysis of particular classes of security problems or their solutions - but are fully aware that it all fits within a wider context of people using systems and communicating data in secure and insecure ways, and how external pressures beyond the mere technology impact on that. The topic of computer security then naturally widens to include topics like privacy, cyber crime, and ethics and law relating to computing, as well as bringing in aspects of psychology, sociology and economics.
From that perspective, the Cyber Security research group played a key role in setting up, and continues to be a core contributor to, the University's Interdisciplinary Cyber Security Research Centre, see www.cybersecurity.kent.ac.uk.
The group has a strong involvement with postgraduate teaching in this area. It teaches most of the core modules in MSc programmes in Computer Security, and Networks and Security. A new (from September 2017) MSc Course in Cyber Security has been provisionally certified by GCHQ. The group is also involved in undergraduate modules in this area, as well as postgraduate programmes in other schools such as the MSc Information Security and Biometrics, and in UK activities to define curricula in Cyber Security.
Members are engaged in the following areas of research (research areas in more detail) .
Data Ethics and Privacy
Data Science is about developing new techniques to better understand data and draws on many areas within and outside of computer science. Our research group develops and applies methods to interpret rich information sources.Our research comes under three themes:
Our graduates have gone on to work in:
Recent graduates have gone on to develop successful careers at leading companies such as:
The University has a friendly Careers and Employability Service, which can give you advice on how to:
You have access to a dedicated Employability Coordinator who is a useful contact for all student employability queries.
You gain practical work experience as part of your degree through our industrial placements scheme. We have a dedicated Placement Team who can give advice and guidance. All our placements are in paid roles.
In previous years, students have worked at a wide range of large and small organisations, including well-known names such as:
You can take your work placement abroad. Previous destinations include Hong Kong and the US.
An industrial placement gives you invaluable workplace experience, which greatly enhances your employment prospects and also helps put your academic learning into a real-world context.
The 2024/25 annual tuition fees for this course are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.* If you are uncertain about your fee status please contact information@kent.ac.uk.
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme, fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.*
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
Find out more about general additional costs that you may pay when studying at Kent.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
Next steps
Learn more about the application process or begin your application by clicking on a link below.
You will be able to choose your preferred year of entry once you have started your application. You can also save and return to your application at any time.
Supporting your success
Kent ranked top 50 in The Complete University Guide 2024.
Fees and funding
Kent has risen 11 places in THE’s REF 2021 ranking, confirming us as a leading research university.
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