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Advanced Topics in Machine Learning

Note: Whilst every effort is made to keep the syllabus and assessment records correct for this course, the precise details must be checked with the lecturer(s).

Code:	M050 (Also taught as: GI13)
Year:	4
Prerequisites:	Probability, calculus, linear algebra, COMPM055 Supervised Learning and COMPM056 Graphical Models
Term:	2
Taught By:	John Shawe-Taylor (50%) Massimiliano Pontil (50%)
Aims:	To learn; 1) Learning and control of stochastic dynamical systems 2) Multitask learning
Learning Outcomes:	To gain in-depth familiarity with the selected research topics, understand their theory and applications, be able to individually read, understand and discuss research works in the field.

Content:

Learning and control of stochastic dynamical systems	Gaussian processes (for regression and classification) Linear Dynamical systems Control Reinforcement Learning Switching Linear Dynamical Systems
Multitask Learning	Multitask and transfer Learning Matrix factorisation. Convex optimisation techniques

Method of Instruction:

Lectures, reading, presentation and associated class problems.

Assessment:

The course has the following assessment components:

Written Examination (2.5 hours, 50%)
Coursework Section (2 pieces, 50%)

To pass this course, students must:

Obtain an overall pass mark of 50% for all sections combined

The examination rubric is:
There will be two sections: section A and B, each with two questions. You should answer just one question from each section.

Resources:

Brian Wandell, Foundations of Vision ( http://www.sinauer.com/detail.php?id=8532 )

C.M. Bishop: Pattern Recognition and Machine Learning. (Springer, 2006)

Carl E. Rasmussen and C.K.I. Williams: Gaussian Processes for Machine Learning (MIT Press, 2006)

You should thoroughly review the maths in the cribsheet provided at the link below before the start of the module. The Matrix Cookbook is also a very helpful resource.

D.J.C. MacKay: Information Theory, Inference and Learning Algorithms. (Cambridge University Press, 2003)

Rasmussen Williams book also available online

MacKay book also available online

Gatsby Maths Cribsheet

Matrix Cookbook

D. Barber: Machine Learning and Graphical Models

Convex Optimisation

Click here for more information and course notes

This page last modified: 26 May, 2010 by Nicola Alexander