Start Date between May 26 - July 27

This module aims to equip students with the mathematical understanding and intuition necessary to interpret, analyse, and design the methods that underpin modern AI. Building on key areas of linear algebra, vector calculus, probability, and analytical geometry, the module connects these concepts to the four foundational pillars of machine learning: classification, regression, density estimation, and dimensionality reduction. The module highlights how these mathematical principles give rise to practical modelling techniques that capture the process of learning from data and underpin approaches across the full spectrum of modern machine learning, from classical statistical models to artificial neural networks and generative AI. Learning activities combine explanatory notes, visual and geometric illustrations, worked examples, and guided problem-solving exercises to progressively develop both conceptual insight and analytical fluency.

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

This module aims to equip students with the mathematical understanding and intuition necessary to interpret, analyse, and design the methods that underpin modern AI. Building on key areas of linear algebra, vector calculus, probability, and analytical geometry, the module connects these concepts to the four foundational pillars of machine learning: classification, regression, density estimation, and dimensionality reduction. The module highlights how these mathematical principles give rise to practical modelling techniques that capture the process of learning from data and underpin approaches across the full spectrum of modern machine learning, from classical statistical models to artificial neural networks and generative AI. Learning activities combine explanatory notes, visual and geometric illustrations, worked examples, and guided problem-solving exercises to progressively develop both conceptual insight and analytical fluency.