By
Balas Natarajan
Description
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results
and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis
of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction
to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as
finite automata and neural networks. The presentation is intended for a broad audience--the author's ability to motivate and pace discussions
for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important
results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and
advanced AI courses. Also an important reference for AI researchers.
