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ENGINEERING TRIPOS PART IIB - 2012/2013

Module 4F13 - Machine Learning

Leader:	Prof Z Ghahramani
Timing:	Lent Term
Prerequisites:	3F3 useful
Structure:	14 lectures + 2 examples classes
Assessment:	*Material / Format / Timing / Marks* Lecture Syllabus / Coursework / to be confirmed / 100%

AIMS

Machine learning (ML) is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world.

The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

LECTURE SYLLABUS

Linear models, maximum likelihood and Bayesian inference
Gaussian distribution and Gaussian process
Model selection
The Expectation Propagation (EP) algorithm
Latent variable models
The Expectation Maximization (EM) algorithm
Dirichlet Distribution and Dirichlet Process
Variational inference
Generative models, graphical models: Factor graphs

Lectures will be supported by Octave/MATLAB demonstrations.

OBJECTIVES

On completion of the module students should:

Have a good understanding of basic concepts in statistical machine learning
Be able to apply basic ML methods to practical problems

REFERENCES

Please see the Booklist for Group F Courses for references for this module.

Last updated: May 2012

teaching-office@eng.cam.ac.uk