The book covers a broad range of topics and approaches in machine learning. As a consequence, the amount of content dedicated to each topic is quite sparse. Decision Trees, Neural Networks, Bayesian Classifiers/Networks, Instance-Based Learning and Genetic Algorithms are all covered in a single book that counts under 400 pages. Since it is written in a concise and intuitive way however, it provides a solid foundation that the reader can build upon if he wishes to go deeper into any subject. Likewise, with this foundation, readers should be able to easily catch up on recent innovations (the book is quite old). Recommended.
I first used this book as the required text for my course in ML in 1997 and got rave reviews from the students. I will be using it again in 1999. I found ALL of the major topics and issues in ML addressed. The book is easily readable with anyone with a computer science background, and the book works quite well in a wide variety of approaches to presentation at the advanced undergraduate and graduate levels.
Everything I will do in the future will be based on ML and just one semester of an ML course & this book has converted me(even though my major is not Comp.Science). Of-course this is due majorly to Dr. Thomas Ioerger and his teaching abilities(Texas A&M), but the book presents all concepts(even seemingly complex ones) in a way that is easy and enjoyable to learn. One of the most useful books I've ever studied!
This book provides a smooth introduction to Machine Learning. It is not too math heavy and can be used easily by people with math cs background. There little golden nuggets of concentrated experience scattered around which makes it even more worthwhile for people just diving in. Each chapter is independent and straight to the point. I highly recommend it