Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.

This module will give you skills in artificial intelligence and data science developed through themes presented by relevant experts across a range of academic departments. It is aimed at strengthening the quantitative skill set of those faced with data challenges in their future professional life.

The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decision using data. This module blends data analysis, decision making and visualisation with practical python programming. Students will need a reasonable programming background as they will be expected to develop a complete end-to-end data science application.

How do you formulate financial decision problems mathematically? And how do you identify an appropriate method of solution? Understand the basic models and mathematical methods underlying modern portfolio management. Assess the limitations of these models and learn to correctly interpret your results from calculations.

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.