Introduction to Machine Learning - 1st Edition - ISBN: 9780080509303

Introduction to Machine Learning

1st Edition

Authors: Yves Kodratoff
eBook ISBN: 9780080509303
Imprint: Morgan Kaufmann
Published Date: 28th June 2014
Page Count: 298
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A textbook suitable for undergraduate courses in machine learning and related topics, this book provides a broad survey of the field. Generous exercises and examples give students a firm grasp of the concepts and techniques of this rapidly developing, challenging subject.

Introduction to Machine Learning synthesizes and clarifies the work of leading researchers, much of which is otherwise available only in undigested technical reports, journals, and conference proceedings. Beginning with an overview suitable for undergraduate readers, Kodratoff establishes a theoretical basis for machine learning and describes its technical concepts and major application areas. Relevant logic programming examples are given in Prolog.

Introduction to Machine Learning is an accessible and original introduction to a significant research area.

Table of Contents

Introduction to Machine Learning
by Yves Kodratoff
9 Learning by Similarity Detection: The "Rational" Approach
    1 Knowledge representation
    2 Description of a rational generalization algorithm
    3 Using axioms and idempotence
    4 A definition of generalization
    5 Use of negative examples
10 Automatic Construction of Taxonomies: Techniques for Clustering
    1 A measure of the amount of information associated with each descriptor
    2 Application of data analysis
    3 Conceptual clustering
11 Debugging and Understanding in Depth: The Learning of Micro-Worlds
    1 Recognition of micro-worlds
    2 Detection of lies
12 Learning by Analogy
    1 A definition of analogy
    2 Winston"s use of analogy
Appendix 1 Equivalence Between Theorems and Clauses
    1 Interpretation
    2 The Herbrand universe of a set of clauses
    3 Semantic trees
    4 Herbrand"s theorem
Appendix 2 Synthesis of Predicates
    1 Motivation: an example of useful synthesis in ML
    2 Synthesis of predicates from input/outputs
    3 Approaches to automatic programming
Appendix 3 Machine Learning in Context
    1 Epistemological reflections on the place of AI in science
    2 Reflections on the social role of ML


No. of pages:
© Morgan Kaufmann 1989
Morgan Kaufmann
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About the Author

Yves Kodratoff

Affiliations and Expertise

University Paris-Sud

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