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Advanced Lectures on Machine Learning

ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3176)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (9 chapters)

  1. Front Matter

  2. Some Notes on Applied Mathematics for Machine Learning

    • Christopher J. C. Burges
    Pages 21-40
  3. Gaussian Processes in Machine Learning

    • Carl Edward Rasmussen
    Pages 63-71
  4. Unsupervised Learning

    • Zoubin Ghahramani
    Pages 72-112
  5. Monte Carlo Methods for Absolute Beginners

    • Christophe Andrieu
    Pages 113-145
  6. Stochastic Learning

    • Léon Bottou
    Pages 146-168
  7. Introduction to Statistical Learning Theory

    • Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi
    Pages 169-207
  8. Concentration Inequalities

    • Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet
    Pages 208-240
  9. Back Matter

About this book

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.

Keywords

  • 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

Editors and Affiliations

  • 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

Bibliographic Information

  • Book Title: Advanced Lectures on Machine Learning

  • Book Subtitle: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures

  • Editors: Olivier Bousquet, Ulrike Luxburg, Gunnar Rätsch

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/b100712

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2004

  • Softcover ISBN: 978-3-540-23122-6Published: 02 September 2004

  • eBook ISBN: 978-3-540-28650-9Published: 22 March 2011

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 246

  • Topics: Artificial Intelligence, Computer Science, Algorithms, Theory of Computation, Automated Pattern Recognition

Buying options

eBook USD 39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions