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

Demonstrate independence and originality on an individual project, specialising in computational management, and put into practice some of the techniques you have learned throughout the course.

Use operational laws to relate different system performance measures and analyse systems scalability.

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

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

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

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.

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

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

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 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.

Acquire foundational knowledge and basic principles of logic-based machine learning and develop skills for formalising logic-based learning tasks.

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.

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.

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.

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

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.

Learn a basic process algebra and the applications of the pi-calculus.

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

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

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.

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

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

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.

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

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

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

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

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