Machine Learning for Engineers

Module code: EG3213

In this module, you will be introduced to the core principles and practical applications of data-driven engineering. You will explore the foundations of pattern recognition, probability and statistical inference before progressing to more advanced topics such as neural networks, dimensionality reduction, and parametric and non-parametric classifiers. You will work with real-world engineering datasets to learn how to apply machine learning algorithms, critically evaluate their performance, and tailor solutions to specific problems. Through regular computer lab sessions, mini-competitions and guided learning activities, you will develop both a strong theoretical understanding and practical experience of data-driven engineering. You will regularly practise implementing machine learning techniques, including Bayesian classifiers, nearest neighbours and principal component analysis, using MATLAB. Alongside these technical skills, you will learn how to make informed decisions about model selection, consider ethical issues in machine learning, and evaluate the wider impact of data analysis and automation.