Aims:

The aims of the module are to introduce students to the key ideas and concept underlying statistical learning theory; and provide students with strong foundations and be instrumental for students planning to undertake research, either within the academic environment via a PhD or directly through Industrial Research.

Learning outcomes:

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

Define mathematically what it means for an algorithm to "learn" (which will be key to obtaining a theoretical understanding of the topics studied across the MSc).
	Implement most modern machine learning algorithms as well as modifying them according to individual application requirements.
	Develop new machine learning algorithms to address novel applications.
	Prove the theoretical properties of such novel machine learning algorithms as well as those of more established approaches.
	Compare the effectiveness both theoretical and practical of alternative options for machine learning algorithms when addressing a given problem, which will prove to be of key relevance when performing model selection in most practical settings.
	Adapt a learning algorithm when it does not achieve the expected performance to improve it.
Content:

This module is designed to introduce students to the main techniques and ideas in statistical learning theory (SLT). This is a foundational module, that provides a deep understanding of how machine learning algorithms are designed, what their limitations are and when statistical guarantees can be derived.

Topics covered include:

Bias-Variance tradeoff.
	Covering Numbers for Hypotheses spaces.
	Rademacher Complexity.
	VC dimension.
	Stability and Generalization.
	Early stopping.
	The double descent effect.
	Stochastic projections as Regularization.
	Transfer-learning.
	Meta-learning.
Requisites:

To be eligible to select this module as an optional or elective, a student must: (1) be registered on a programme and year of study for which it is formally available; (2) have a strong understanding of Linear Algebra, Multivariable Calculus, Probability Theory; (3) have experience in developing proofs for “discrete and continuous” mathematical propositions; and (4) have taken Introduction to Machine Learning (COMP0088) or Supervised Learning (COMP0078) or be able to demonstrate equivalent experience.