Advanced Lectures on Machine Learning

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.

• algorithmic learning
• bayesian inference
• classification
• classifier systmes
• inductive inference
• learning
• learning algorithms
• learning theory
• machine learning
• statistical learning
• stochastic learning
• unsupervised learning
• algorithm analysis and problem complexity

• Pertinence, Paris, France

  Olivier Bousquet

• Max Planck Institute for Biological Cybernetics, Tübingen, Germany

  Ulrike Luxburg

• Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany

  Gunnar Rätsch

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