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.

This module offers a comprehensive overview of optimisation techniques, beginning with fundamental mathematics and progressing through basic and stochastic search strategies, including Gradient Descent, Random Search, Grid Search, and Simulated Annealing. Students will learn about constraint handling with penalties, mathematical programming, and critically evaluate contemporary research. The curriculum covers evolutionary algorithms such as Genetic Algorithms and Genetic Programming, addresses multi-objective problems and decision-making, and explores graph problems with Ant Colony Optimisation. The module also also covers human factors and ethical considerations in optimisation, preparing students for advanced research or professional practice in the field.