Transcription of INTRODUCTION MACHINE LEARNING
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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 .. 11. Sources .. 13. Bibliographical and Historical Remarks .. 13. 2 Boolean Functions 15. Representation.
associated with arti cial intelligence (AI). Such tasks involve recognition, diag-nosis, planning, robot control, prediction, etc. The \changes" might be either enhancements to already performing systems or ab initio synthesis of new sys-tems. To be slightly more speci c, we show the architecture of a typical AI 1
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