new Machine Learning book

[Tom Mitchell]

 NEW COMPREHENSIVE TEXTBOOK:  Machine Learning, Tom Mitchell, McGraw Hill

McGraw Hill announces immediate availability of MACHINE LEARNING, a
new textbook that provides a thorough, multi-disciplinary introduction
to computer algorithms for automated learning.  The chapter outline
is:

     1. Introduction 
     2. Concept Learning and the General-to-Specific Ordering 
     3. Decision Tree Learning 
     4. Artificial Neural Networks 
     5. Evaluating Hypotheses 
     6. Bayesian Learning 
     7. Computational Learning Theory 
     8. Instance-Based Learning 
     9. Genetic Algorithms 
     10. Learning Sets of Rules 
     11. Analytical Learning 
     12. Combining Inductive and Analytical Learning 
     13. Reinforcement Learning

     (414 pages)

This book is intended for upper-level undergraduates, graduate
students, and professionals working in the area of neural networks, machine
learning, datamining, and statistics.  It includes over a hundred homework
exercises, along with web-accessible code and datasets (e.g., neural
networks applied to face recognition, Bayesian learning applied to
text classification).

For further information and ordering instructions, see
http://www.cs.cmu.edu/~tom/mlbook.html