Our machine learning and AI teaching

The Department's teaching in machine learning and AI is aimed at 3rd year BSc students, MSc students, and doctoral students. It is mainly offered in our following degrees: BSc in Data Science, BSc in Economics and Data Science, BSc in Mathematics, Statistics and Business; MSc in Data Science, MSc in Statistics; MPhil/PhD Statistics.

Our courses cover a broad range of subjects, including learning algorithms ranging from traditional machine learning techniques (ST310), with a particular focus on statistical and foundational methodologies (ST405, ST443, ST510) to deep learning (ST311, ST456), and the broader field of AI (ST449). The following sub-areas of ML are covered by dedicated courses: Reinforcement Learning (ST455), Bayesian ML (ST451), and ML on graph data (ST457). Finally, ST313 covers deals with the ethics of AI and its responsible development, contextualising the subjects within the broader social science focus of the School.

· ST310 Machine Learning

· ST311 Artificial Intelligence

· ST313 Ethics for Data Science

· ST405 Unsupervised Machine Learning and Multivariate Data Analysis

· ST443 Machine Learning and Data Mining

· ST449 Artificial Intelligence

· ST451 Bayesian Machine Learning

· ST455 Reinforcement Learning

· ST456 Deep Learning

· ST457 Graph Data Analytics and Representation Learning

· ST510 Foundations of Machine Learning

From 2026, master's students enrolled in the ST446 – Distributed Computing for Big Data course have access to courses and learning resources offered by Amazon as part of the AWS Academy curriculum. Four courses are offered covering cloud computing fundamentals, data engineering, machine learning, and natural language processing. Along with other cloud computing resources in use since 2018, the AWS Academy curriculum will equip students with highly sought-after cloud and AI / ML -skills.