Synoposis: This is a fast-paced graduate class on machine learning, covering the foundations, such as (Bayesian) statistics and information theory, as well as a brief coverage of supervised learning (classification, regression), and a more in-depth coverage of unsupervised learning (clustering, dimensionality reduction, graphical models).
Textbook: Draft copies of my textbook, Machine Learning: a probabilistic approach, will be made available for purchase on Jan 2nd, 2010, for about $60.
Pre-requisites. Linear algebra, calculus, probability theory, programming (preferably Matlab), some undergrad class on machine learning (eg CS 340) or statistics (eg Stat 306). I also strongly recommend CPSC 542G (topics in numerical computation), Robert Bridson, Fall 2009, MW 11-12.30.