Demonstrate independence and originality on an individual project, specialising in an area of visual computing and/or robotics.

Explore how images are formed, how they are represented on computers and how they can be processed by computers to extract semantic information.

Examine the approaches that enable the rapid and systematic design of custom computers and learn how to analyse the performance of a custom computer.

Develop a thorough understanding of high-performance and energy-efficient computer architecture as a basis for informed software performance engineering.

Assess the exciting field of mobile robotics, at a time when cutting-edge robots are beginning to leave the research laboratory to tackle real-world tasks.

Advance your knowledge of the concepts that enable the design and implementation of scalable distributed systems.

Learn how to describe the core principles of autonomous systems learning and calculate mathematical solutions to problems using reinforcement learning theory.

Explore the fundamental concepts and advanced methodologies of deep learning and relate them to real-world problems.

Examine how probability can be used to make decisions by a computer and advance your understanding of inference networks and linear and non linear methods in statistical pattern recognition.

Learn how to design and implement modern statistical machine learning methodologies, as well as inference mechanisms.

Gain insights into the different types of problems that exist in machine learning and the basic algorithms used to address them.

Obtain theoretical and practical knowledge of how database management systems (DBMS) are programmed in SQL and how SQL queries are optimised.

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

Discover how cryptographic techniques can be used to design and implement secure communicating systems for a variety of different needs and applications.

Gain an in-depth understanding of thestate-of-the-art of logic-based learning, starting from its key foundation concepts and principles before moving to more recent advances.

Understand basic concepts of computer graphics; and familiarise yourself with the mathematical principles used for computer generated imagery, shading and light approximations.

Study network and web security broadly from the network to the application layer and understand the main threats, attack techniques and defences relevant to cybersecurity and network security.

Analyse how performance engineering ensures computer systems are responsive, scalable, and efficient and explore the fundamental principles and techniques used to achieve these goals. 

Unpick the key concepts, problems and results in distributed algorithms and gain practical experience of programming them.

Study quantitative methods for decision making in areas including computer science, economics, logistics and industrial engineering.

Analyse in detail the design of type assignment systems for programming languages and focus on the importance of a sound theoretical framework.

Use separation logic to reason compositionally about sequential and concurrent heap-manipulating programs.

Become familiar with the concepts and techniques underlying privacy-enhancing technologies across a variety of areas.

Understand the basic concepts of quantitative finance and financial engineering and build awareness of the major decision, hedging, and pricing problems in finance.

Discover how to describe the complexity classes associated with computational problems, and how to fit a particular problem into a class of related problems.

Assess techniques and tools for improving the reliability of software systems and discuss their respective strengths and weaknesses.

Deepen your knowledge of the themes and challenges of host-level native, web, and mobile security and privacy.

Analyse the foundational principles behind decentralised ledgers and apply them to current research in cryptocurrencies.

Learn how to design program analyses for classical and probabilistic programs and how to solve the resulting equations and constraints.

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

Acquire the techniques and tools needed to devise and develop natural language processing (NLP) components and applications.

Discover why knowledge representation and reasoning are sential components of an intelligent system and are at the core of artificial intelligence research.

Develop intellectual and practical skills in the use of modal logics for knowledge representation and automated reasoning in Artificial Intelligence.

Study an advanced computer science topic of your choice and plan, write and present a scientific report.

Become familiar with Prolog and develop the knowledge and skills required to write small Prolog applications with an artificial intelligence dimension.

Engage with the emerging field of robot learning and discover how robots can acquire skills and control their bodies using machine learning techniques.

Assess the best decision algorithms for solving real-life problems arising in computer science, computational management and economics.

Analyse topics of advanced robotics, with a focus on real-time state estimation and mapping.

Become familiar with separation logic and associated verifications tools, and learn how to write program specifications using these tools.

Cover fundamental concepts of machine learning for imaging and relate them to real-world problems in computer vision and medical image analysis.

Learn about state of the art approaches to attack smartphone and internet of things (IoT) systems and practise reverse engineering and attack techniques for smartphone apps.

Explore embedded technologies that make up the internet of things and appreciate the many architectural aspects and trade-offs in the design of a pervasive system.

Advance your understanding of information and coding theory, particularly in relation to computer security.

Analyse areas of probabilistic model checking and probabilistic program analysis and become proficient using this modelling.

Learn about the design and implementation of object-oriented languages and the rationale for various language features and alternatives.

Work on the active artificial intelligence research area of argumentation and gain familiarity with machine arguing concepts.

Study basic notions of quantum computing and mechanics from a computer science viewpoint.