MSc Intelligent Systems and Robotics

What fascinates you? Apply your learning in computer science or engineering to solve a problem. Design, implement and evaluate a solution, producing a dissertation on your investigation and giving an oral presentation of your work. Test your knowledge, while gaining practical experience and building your project management skills.

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Computer vision is the discipline that tries to understand the content of images and videos. It has an extraordinarily wide range of applications; well-known ones include inspection on production lines, reading number plates, mixing live and computer-generated action in movies, and recognising faces. However, researchers are working on applications such as driverless cars, building 3D models from photographs, robot navigation, gaming interfaces, and automated medical diagnosis -- in fact, whenever you as a human looks at the world and try to understand what you see is fair game for computer vision.This module introduces you to the principles of computer vision through a series of lectures and demonstrations. You have an opportunity to learn how to use these principles and algorithms on real-world vision problems in the associated laboratories using the industry-standard toolkit, OpenCV.

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Teamwork skills are essential for employability. The aim of this module is to provide students with the opportunity to apply their specialised knowledge to a realistic problem and gain practical experience of the processes involved in the team-based production of software. Wherever possible, teams are organised on the basis of shared interest, and the problem is designed to exercise their understanding of their area of specialised study. Starting from an outline description of a realistic problem, each team is required to develop a fully implemented software solution using appropriate engineering and project management techniques.

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This module gives an introduction to intelligent systems and robotics. It goes on to consider the essential hardware for sensing and manipulating the real world, and their properties and characteristics. The programming of intelligent systems and real-world robots are explored in the context of localisation, mapping, and fuzzy logic control.

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Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.

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This module aims to prepare students for conducting an independent research project leading to a dissertation and to provide them with an appreciation of research and business skills related to their professional career. As a precursor to their project students, individually select an area of Computer Science, or Electronic Engineering, or Computational Finance and perform the necessary background research to define a topic and prepare a project proposal under the guidance of a supervisor. The module guides them by a) introducing common research methods b) creating an understanding of basic statistics for describing and making conclusions from data c) helping to write a strong proposal including learning how to perform literature search and evaluation and d) giving an in-depth view into the business enterprise, financial and management accounting and investment appraisal.

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Wish to design, program and evaluate embedded systems from software specification to hardware implementation? Study the techniques to develop software for embedded systems and robotics. Examine performance needs and the key issues in designing real-time software for embedded systems in real-world applications. Understand the main techniques of real-time programming.

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The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.

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Acquire critical and transferable skills associated with the creation and growth of new business ventures. You focus on the development process from start up to early stage growth of new ventures, new small businesses spin offs from large firms, and especially innovative, technology-based firms. You study opportunity identification, self-efficacy, ideas generation, bricolage and bootstrapping, developing business models, networking, marketing, and finance.

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This module provides first-hand experience of the design simulation and production of complex electronic circuits. A word specification is provided for a consumer electronics device for which a prototype is designed using reference and first principles. The circuit is then simulated and tested in Multisim to verify operation. Once satisfactory, a hardware prototype is developed on a prototype medium e.g. breadboard and tested in real-world conditions. Then using PCB design software, a PCB is designed and populated to produce the final product. The module has a large emphasis on the practical with a lighter emphasis on the theoretical.

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This module examines the nature of fun and engagement in the context of game design, and includes the study of how to integrate narrative into gameplay and how to criticise game design. This module also covers how to design and deploy objective measures of player experience and how to apply these to analyse game logs in a number of case studies. The effects of game AI on player experience are also considered.

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Many of today’s best computer games rely on realistic physics at the core of their gameplay. In this course, students are taught how these physics engines work, and how to create physics-based games of their own. Students create a physics engine from scratch, and also learn how to use existing industry-standard open-source 2-D and 3-D physics engines. The necessary principles of physics and mathematics are taught, assuming very little prior knowledge. Vectors, matrices, and numerical integration are taught on a need-to-know basis, with code examples to illustrate the methods. Each lecture is followed by a lab session, where the new techniques are programmed by each student. Almost immediately, students will create scenarios where objects are moving and bouncing around the screen realistically. Each lab session ends in creating a small physics-based game. The course is assessed through tests, and a larger game-programming assignment.

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This module covers a range of Artificial Intelligence techniques employed in games, and teaches how games are and can be used for research in Artificial Intelligence. The module explores algorithms for creating agents that play classical board games (such as chess or checkers) and real-time games (Mario or PacMan), including single agents able to play multiple games. The course also covers Procedural Content Generation, and explores the techniques used to simulate intelligence in the latest videogames.

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The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decision using data. This module blends data analysis, decision making and visualisation with practical python programming.Students will need a reasonable programming background as they will be expected to develop a complete end-to-end data science application.

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