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.

This module will enable you to expand your knowledge on multiple statistical methods. You will learn the concepts of decision theory and how to apply them, have the chance to explore Monte Carlo simulation, and develop an understanding of Bayesian inference, and the basic concepts of a generalised linear model.

Ever considered becoming an Actuary? This module covers the required material for the Institute and Faculty of Actuaries CT4 and CT6 syllabus. It explores the stochastic process and principles of actuarial modelling alongside time series models and analysis.

Why are arbitrage arguments important in modern finance? How can a binomial model evaluate derivatives? What are the main models for interest rates? Understand the mathematical techniques underlying the modelling of derivative pricing. Acquire skills in the development of pricing and risk management. Explore stochastic methods and credit risk.

This module focuses on Bayesian and computational statistics. You will develop your understanding of Bayes’ theorem and Bayesian statistical modelling, and Markov chain Monte Carlo simulation, by developing algorithms for simple probability distributions.

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.