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INTRODUCTION MACHINE LEARNING

INTRODUCTION . TO. MACHINE LEARNING . AN EARLY DRAFT OF A PROPOSED. TEXTBOOK. Nils J. Nilsson Robotics Laboratory Department of Computer Science Stanford University Stanford, CA 94305. e-mail: November 3, 1998. Copyright 2005. c Nils J. Nilsson This material may not be copied, reproduced, or distributed without the written permission of the copyright holder. ii Contents 1 Preliminaries 1. INTRODUCTION .. 1. What is MACHINE LEARNING ? .. 1. Wellsprings of MACHINE LEARNING .. 3. Varieties of MACHINE LEARNING .. 4. LEARNING Input-Output Functions .. 5. Types of LEARNING .. 5. Input Vectors .. 7. Outputs .. 8. Training Regimes .. 8. Noise .. 9. Performance Evaluation .. 9. LEARNING Requires Bias .. 9. Sample Applications.

current nets, radial basis functions, grammar and automata learning, genetic algorithms, and Bayes networks :::. I am also collecting exercises and project suggestions which will appear in future versions. My intention is to pursue a middle ground between a theoretical textbook and one that focusses on applications. The book concentrates on the ...

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