Teaching

We live in the age of data: it is ubiquitous in modern life. However, data in itself cannot inform or inspire. We need to do science with the data to extract knowledge and actionable insights. In this module, we will explore scientific methods and processes that make data so valuable to us and society and gain an insight into the world of practical data science and its challenges. We will also cover the ethical issues relating to data.

Optimisation is at the core of many disciplines. Whether we want to improve the performance of a machine learning model, increase the efficiency of an aircraft design, or simply reduce the costs of productions in a business operation, we must deploy computational optimisation methods for achieving the best results. In this module, we will cover mathematical and algorithmic fundamentals of optimisation, including derivative and derivative-free approaches for both linear and non-linear problems. We will also discuss advanced topics, such as multi-objective optimisation, handling uncertainty, principled methods when problem evaluations are computationally expensive, and performance comparison between stochastic optimisers, in the context of real-world problems.