Transcription of INTRODUCTION MACHINE LEARNING
{{id}} {{{paragraph}}}
Example: bankruptcy
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.
and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models.
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Chapter 12 Bayesian Inference, Statistical Machine Learning CHAPTER 12. BAYESIAN INFERENCE, AN INTRODUCTION TO MACHINE LEARNING, Statistical, Machine, Statistical learning, Distributed Optimization, Machine learning, Learning, Statistical Machine, Lecture Notes, About the Tutorial, Introduction to Statistical Learning Theory