Advanced Topics in Statistical Machine Learning COMP4680

Course overview

Assumed knowledge & required skills

Knowledge of machine learning at the level of COMP4670 Introduction to SML
Familiarity with linear algerba (including norms, inner products, determinants, eigenvalues, eigenvectors, and signular value decomposition)
Familiarity with multivariate differential calculus (e.g., derivative of a vector-valued function)
Exposure to mathematical proofs

Course description

This course explores a selected area relevant to statistical machine learning in depth, and will be taught by an SML staff member of internationally recognised standing and research interest in that area. Based on current SML staffing, this will be one of:

kernel methods
graphical models
reinforcement learning
convex analysis
optimisation
bioinformatics
minimal description length principle
topics in information theory
decision theory

Course content

In Semester 2, 2013 we will be teaching convex analysis and optimisation.

Textbooks

Main text:

Stephen Boyd and Lieven Vandenberghe, "Convex Optimization"

Reference texts:

Hiriart-Urruty and Lemaréchal, “Fundamentals of Convex Analysis”
Bertsekas, Nedic and Ozdaglar, “Convex Analysis and Optimization”
Bertsekas, “Nonlinear Programming”