Advanced Computing

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.

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

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.

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

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

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

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

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

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

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

Discover the basic notions of quantum computing and be introduced to Quantum Bits, Quantum Entanglement and Quantum Algorithms.

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

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

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

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

Develop your knowledge of machine learning further by relating back to real-world problems in computer vision and medical image analysis.

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.

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.

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

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

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

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

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

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

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.

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

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. 

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 the rationale behind the theoretical end of computational neuroscience and basic principles of the tools needed to simulate the brain's intelligent behaviour in this interdisciplinary module.

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.

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.

Delve into the deep connections between information theory, statistics and machine learning, in this module linking computing, statistics and geometry. Includes mathematical and computational exercises, and provides crucial theoretical background for a career in data science or machine learning.

In this course, you will learn how to build reliable systems that use machine learning, from data ingestion to user experience. You will gain hands-on experience running a simplified ML system in a simulated real world setting.

Learn how modern networked systems are built and gain hands-on experience with their design and implementation. You will explore new architectures, hardware and software infrastructure, and core principles such as layering and the end-to-end argument.

Learn to build machine learning models that work with non-Euclidean data.

You will study embeddings, optimization techniques from Riemannian geometry, and graph theory, and apply neural networks designed for data on curved spaces and manifolds.

Explore why some problems are hard to solve but easy to check, including the famous P vs NP question. You will learn simple tools from circuit and proof complexity to tackle these challenges.

Investigate how modern hardware and systems make machine learning work in practice. From GPUs and TPUs to efficient ML techniques and large-scale deployment, you will get hands-on experience running models across cloud and edge environments.

This module covers the deep learning methods and systems architectures driving modern generative AI.