Aims:

The module aims to empower the students with the mathematical framework, tools, and reasoning to analyse and understand how machine learning algorithms are operating and behaving. This aims at developing generations of practitioners of machine learning algorithms who are aware of the foundational concepts and are comfortable with the implications and limitations of the deployment of such algorithms, notably in sustainable development or healthcare applications.

Learning outcomes:

On successful completion of the module, a student will be able to: 

Demonstrate understanding the foundations of artificial intelligence, relating in particular to statistical learning theory and theoretical machine learning. 
Evaluate the settings under which machine learning algorithms enjoy certain desirable properties, such as sparsity, reduced environmental footprints, privacy-preserving capabilities, to name but a few. 
Describe the foundations of artificial intelligence and how those foundations are crucial to understand and deploy intelligent systems in the real world.
Content:

This module covers the fundamentals of artificial intelligence and offers a gentle yet thorough introduction to the mathematics underpinning machine learning. From theoretical and algorithmic perspectives. The module starts with an introduction to statistical learning theory, including the notions of risk, generalisation bounds, model complexity (VC dimension, Rademacher complexity, ...), bias-variance trade-off, overfitting, regularisation, and many others. Classical results will be proved and discussed. The second half of the module will present classical machine learning algorithms, such as linear regression, penalised regression (Lasso, Ridge), decision trees and random forests, boosting, k-nearest neighbours, support vector machines, Bayesian methods and probabilistic modelling, deep neural networks and principal component analysis, to name but a few. The module will introduce some key algorithms which will be covered in greater depths in other modules – the module aims at providing an overview rather than diving into details. 

Requisites:

To be eligible to select this module as an optional or elective, a student must be registered on a programme and year of study for which it is formally available.