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.
by Yves Kodratoff
- 1 Why Machine Learning and AI: The Contributions of AI to Learning Techniques
2 Various sorts of learning
2 Theoretical Foundations for Machine Learning
- 0 Theoretical foundations for theory-haters
1 Clauses
2 Unification
3 Resolution and inference on a set of clauses
4 The Knuth-Bendix algorithm
3 Representation of Complex Knowledge by Clauses
- 1 Some examples of logical knowledge representation
2 The transformation of a given sentence into a theorem
3 Representation of a hierarchy during resolution
4 Representation by "ternary quantified trees"
4 Representation of Knowledge about Actions and the Addition of New Rules to a Knowledge Base
- 1 Truth maintenance
2 Predicates in action mode/checking mode
3 Main rules and auxiliary rules
4 Organization of the program for the representation of actions
5 The case of a new rule having the same premise as an old one
6 New rule more specific than an old one
7 Combination of rules
8 Generalization of rules
9 Rules for inference control
5 Learning by Doing
- 1 The problem
2 Version spaces (Mitchell 1982) seen as focusing
3 Application to rule acquisition
4 Learning by trial and error
6 A Formal Presentation of Version Spaces
- 1 Different definitions of generalization
2 Version spaces
7 Explanation-Based Learning
- 1 Inductive versus deductive learning
2 Intuitive presentation of EBL
3 Goal regression
4 Explanation-based generalization
5 Explanation-based learning
8 Learning by Similarity Detection: The Empirical Approach
- 1 General definitions
2 Description of the whole example
3 Recognition
4 Sparseness and the selection criteria for a "good" function
5 The procedure of "emptying the intersections"
6 Creation of recognition functions
9 Application to soybean pathology
10 Application to an algorithm for conceptual clustering
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
Bibliography
Index
