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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Training Regimes . . . . . . . . . . . . . . . . . . . . . . . 8.

humans learn. But there are important engineering reasons as well. Some of these are: Some tasks cannot be de ned well except by example; that is, we might be able to specify input/output pairs but not a concise relationship between inputs and desired outputs. We would like machines to be able to adjust

  Engineering, Machine, Learning, Machine learning