Aims:

The module aims to introduce the students to some of the more advanced and technically challenging topics in Machine Learning, typically including the theory and applications of kernel methods and another topic of choice selected for the given year (such as convex optimisation, statistical learning theory, transfer learning, meta learning, online learning.)

Learning outcomes:

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

Gain in-depth familiarity with the selected research topics; understand how to design and implement learning algorithms.
	Individually read, understand and discuss research papers in the field.
Content:

The module consists of two parts; each part is organised around a research topic in Machine Learning.

Part 1:

Advanced introduction to kernel methods -

Definition of a kernel, how it relates to a feature space, The reproducing kernel Hilbert space.
	Simple applications: kernel PCA, kernel ridge regression.
	Distance between means in RKHS, integral probability metrics, the maximum mean discrepancy (MMD), two-sample tests.
	Choice of kernels for distinguishing distributions, characteristic kernels.
	Covariance operator in RKHS: proof of existence, definition of norms (including HSIC, the Hilbert-Schmidt independence criterion).
	Application of HSIC to independence testing.
	Feature selection, taxonomy discovery.
	Introduction to independent component analysis, kernel ICA.
	Large margin classification, support vector machines for classification.
Part 2:

A second research topic of research, such as convex optimisation, statistical learning theory, transfer learning, meta learning, online 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 a formally available; (2) have high competency with Multivariable Calculus, Probability and Combinatorics, and Linear Algebra such that they can reprove basic results as well as novel results; and (3) have taken at least one introductory machine learning module, for example Supervised Learning (COMP0078) or Introduction to Machine Learning (COMP0088) (or be concurrently enrolled in such a module).