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| STUDENTS > Approximate Inference and Learning in Probabilistic Models |

Approximate Inference and Learning in Probabilistic Models

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:	GI16
Year:	MSc
Prerequisites:	COMPGI18 Probabilstic and Unsupervised Learning
Term:	1
Taught By:	Maneesh Sahani (Gatsby Computational Neuroscience Unit) (50%) YeeWhye Teh (Gatsby Computational Neuroscience Unit) (50%)
Aims:	The module will present the foundations of approximate inference and learning in probabilistic graphical models (e.g. Bayesian networks and Markov networks), with particular focus on models composed from conditional exponential family distributions. Both stochastic (Monte Carlo) methods and deterministic approximations will be covered. The methods will be discussed in relation to practical problems in real-world inference in Machine Learning, including problems in tracking and learning.
Learning Outcomes:	Students will be able to understand how to derive and implement state-of-the-art approximate inference techniques and be able to make contributions to research in this area.

Content:

Monte Carlo methods	rejection and importance sampling sequential Monte Carlo (particle filters) Gibbs sampling Metropolis Hastings Hybrid Monte Carlo Slice Sampling
Deterministic approximations	Mean Field theories based on Kullback-Leibler divergence minimisation structured mean field approximations expectation propagation loopy- and other variants of belief propagation the Laplace approximation general convex approximation

Method of Instruction:

Lecture presentations with associated class problems.

Assessment:

The course has the following assessment components:

Written Examination (2.5 hours, 50%)
Coursework Section (1 piece, 50%)

To pass this course, students must:

Obtain an average of at least 50% when the coursework and exam components of a course are weighted together

The examination rubric is:
Answer all questions

Resources:

There is no required textbook. However, the following in an excellent sources for many of the topics covered here. David J.C. MacKay (2003) Information Theory, Inference, and Learning Algorithms, Cambridge University Press. (also available online)

Web resources for Gatsby Unit courses

This page last modified: 24 August, 2009 by Nicola Alexander