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 .

1.1 Introduction 1.1.1 What is Machine Learning? Learning, like intelligence, covers such a broad range of processes that it is dif- cult to de ne precisely. A dictionary de nition includes phrases such as \to gain knowledge, or understanding of, or skill in, by study, instruction, or expe-

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