ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

by VINOD CHANDRA S.S., ANAND HAREENDRAN S.

Overview

Primarily intended for the undergraduate and postgraduate students of computer science and engineering, this text bridges knowledge gaps in artificial intelligence and machine learning.This book includes numerous case studies and worked out examples. The text begins with an introduction to AI; this is followed by discussion of heuristics searching and game playing. The machine learning section begins with the basis of learning, before moving on to the various association rule learning algorithms. Types of learning, such as reinforced, supervised, unsupervised and statistical are also included, with numerous case studies and application exercises. The algorithms and pseudo codes for each topic make this book particularly useful for students.Key features:Includes case studies for each machine learning algorithm.Incorporates day to day examples and pictorial representations for a deeper understanding.Helps students to easily create programmes.Read more

Product Details

ISBN-13:	9788120349346
Publisher:	PHI Learning
Publication date:	03/06/2014
Sold by:	Barnes & Noble
Format:	eBook
File size:	10 MB

About the Author

Chandra S.S., Vinod VINOD CHANDRA S.S., PhD, is Director, Computer Centre, University of Kerala, Thiruvananthapuram. He is leading many e-Governance projects associated with universities and Kerala Government. With more than a decade of teaching experience in various engineering colleges in Kerala, he has published more than fifty research papers on a wide range of topics in Machine Intelligence. He has also authored three established books. He is a reviewer of many international journals and chair of many International conferences. His research areas include Machine learning algorithms and Computational biology. Hareendran S., Anand ANAND HAREENDRAN S., is associated with Department of Computer Science and Engineering, College of Engineering, Kulathoor, Sreekaryam, Trivandrum. He has presented several research papers in both national and international conferences in the field of machine learning. His research area is performance evaluation of machine learning algorithms

Table of Contents

Preface • Acknowledgements 1. INTRODUCTION 2. HEURISTIC SEARCH TECHNIQUES 3. GAME PLAYING 4. KNOWLEDGE REPRESENTATION 5. KNOWLEDGE REPRESENTATION STRUCTURES 6. REASONING 7. LEARNING 8. ASSOCIATION LEARNING 9. CLUSTERING 10. REINFORCEMENT LEARNING 11. STATISTICAL LEARNING 12. ARTIFICIAL NEURAL NETS 13. SUPERVISED LEARNING 14. UNSUPERVISED LEARNING 15. EXPERT SYSTEMS Index