The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them.

Everything in the world is linked together, and this module introduces the theory of networks which illuminates these mysterious links. You will begin with an introduction to the most common types of network and their mathematical properties, as well as typical network models. You will also use Python/R methods to model and analyse real-world networks.

In this module you will learn techniques underpinning algorithms for studying integer-valued systems, and apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms.

Examine key definitions, proofs and proof techniques in graph theory. Gain experience of problems connected with chromatic number. Understand external graph theory, Ramsey theory and the theory of random graphs.

This module introduces Artificial Intelligence (AI), the science of making computers and machines display intelligent behaviour. This multidisciplinary activity draws from computer sciences, mathematics and statistics, and also elements of philosophy, logic and even psychology. Today, AI is ubiquitous in society, from self-driving cars to spam filters and finance trading to video games. The increasing dependence on AI will reshape society and economy. Understanding AI principles, applications, and limitations is important for all students, regardless of their background, and this module assumes no prior knowledge. This module provides both theoretical and practical techniques, covering AI theory and fundamentals of machine learning models, as well as their implementation and applications.

Are you interested in understanding how AlphaGo was able to beat a top Go player? In this module, you will learn about the models behind successful stories of Reinforcement Learning, where a machine (agent) makes sequential decisions to reach an optimal goal. The lectures will be complemented with Lab sessions where we will take advantage of the Open AI Gym environments, allowing us to train our agents to perform tasks such as playing videogames (Atari) and more.