Examine the tools and techniques that software engineers use on a daily basis to successfully build and maintain large software systems.

Demonstrate independence and originality on an individual project, specialising in an area of software engineering.

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.

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.

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

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 intellectual and practical skills in the use of modal logics for knowledge representation and automated reasoning in artificial intelligence.

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

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

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

Gain an in-depth understanding of the state-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.

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

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

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

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

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

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 essential components of an intelligent system and are at the core of artificial intelligence research.

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.

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

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

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

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

Explore the role of quantum computers in providing efficient solutions to problems that are currently computationally intractable