Computing (Visual Computing and Robotics)

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

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

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

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

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.

Explore the intersection of robotics and human-computer interaction, covering user-centric and user study design, data analysis, and relevant theoretical foundations, in this exciting new field.

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

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

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

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

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

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

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.

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

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

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.

Gain a flavour of different aspects of the broad and ever-expanding field of graph theory and learning, including conventional graph data analysis methods and the nascent field of graph neural networks.

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

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.

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. 

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

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

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

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.

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

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

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

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

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

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

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.

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