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

Olivier Bousquet

ISBN 10: 3540231226 / ISBN 13: 9783540231226

Published by Springer Berlin Heidelberg, 2004

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783540231226

Bibliographic Details

• Title: Advanced Lectures on Machine Learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2004
• Language: English
• ISBN 10: 3540231226
• ISBN 13: 9783540231226
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.