INTRODUCTION MACHINE LEARNING

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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.

I hope that future versions will cover Hop eld nets, Elman nets and other re-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.

  Machine, Learning, Machine learning, Hop eld