A textbook suitable for undergraduate courses in machine learningand related topics, this book provides a broad survey of the field.Generous exercises and examples give students a firm grasp of theconcepts and techniques of this rapidly developing, challenging subject. Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginning with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog. Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.
Klappentext
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.
Inhalt
1 Why Machine Learning and AI: The Contributions of AI to Learning Techniques
2 Theoretical Foundations for Machine Learning
3 Representation of Complex Knowledge by Clauses
4 Representation of Knowledge about Actions and the Addition of New Rules to a Knowledge Base
5 Learning by Doing
6 A Formal Presentation of Version Spaces
7 Explanation-Based Learning
8 Learning by Similarity Detection: The Empirical Approach
9 Learning by Similarity Detection: The 'Rational' Approach
10 Automatic Construction of Taxonomies: Techniques for Clustering
11 Debugging and Understanding in Depth: The Learning of Micro-Worlds
12 Learning by Analogy
Appendix 1 Equivalence Between Theorems and Clauses
Appendix 2 Synthesis of Predicates
Appendix 3 Machine Learning in Context