Introduction to Machine Learning

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

# Machine Learning and Algorithms # Computer Science # AI-based Problem Solving and Planning # Logic, Reasoning, and Knowledge

Yves Kodratoff (1988). "Introduction to Machine Learning". CERN Document Server (European Organization for Nuclear Research).