Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 – 14, 2003, Tübingen, Germany, August 4 – 16, 2003, Revised Lectures

Elad Yom-Tov (auth.), Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch (eds.)9783540231226, 9783540286509, 3540231226

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600.

This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references.

Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Table of contents :
Front Matter….Pages –
An Introduction to Pattern Classification….Pages 1-20
Some Notes on Applied Mathematics for Machine Learning….Pages 21-40
Bayesian Inference: An Introduction to Principles and Practice in Machine Learning….Pages 41-62
Gaussian Processes in Machine Learning….Pages 63-71
Unsupervised Learning….Pages 72-112
Monte Carlo Methods for Absolute Beginners….Pages 113-145
Stochastic Learning….Pages 146-168
Introduction to Statistical Learning Theory….Pages 169-207
Concentration Inequalities….Pages 208-240
Back Matter….Pages –