Machine Learning

I am a graduate student at a major research university. I am currently taking my fifth AI/Machine Learning graduate course. This is the one book everyone grabs for when they need a reference. I had to mark the spine of my book with tape so I could find it more easily on my colleagues shelves. Other books are either not as accessible or too niche-specific. This is the only book out there that covers all of the major machine learning techniques (with the possible exception of support vector machines) and covers them in a manner that can be well understood.Every discipline has one book that must be on your shelf. If you are planning on doing serious research in Machine Learning - this is the one book.

I agree with some of the previous reviews which criticize the book for its lack of depth, but I believe this to be an asset rather than a liability given its target audience (seniors and beginning grad. students). The average college senior typically knows very little about subjects like neural networks, genetic algorithms, or Baysian networks, and this book goes a long way in demystifying these subjects in a very clear, concise, and understandable way. Moreover, the first-year grad. student who is interested in possibly doing research in this field needs more of an overview than to dive deeply into one of the many branches which themselves have had entire books written about them. This is one of the few if only books where one will find diverse areas of learning (e.g. analytical, reinforcment, Bayesian, neural-network, genetic-algorithmic) all within the same cover.But more than just an encyclopedic introduction, the author makes a number of connections between the different paradigms. For example, he explains that associated with each paradigm is the notion of an inductive-learning bias, i.e. the underlying assumptions that lend validity to a given learning approach. These end-of-chapter discussions on bias seem very interesting and unique to this book. Finally, I used this book for part of the reading material for an intro. AI class, and received much positive feedback from the students, although some did find the presentation a bit too abstract for their undergraduate tastes

This book is completely worth the price, and worth the hardcover to take care of it. The main chapters of the book are independent, so you can read them in any order. The way it explains the different learning approaches is beautiful because: 1)it explains them nicely 2)it gives examples and 3)it presents pseudocode summaries of the algorithms. As a software developer, what else could I possibly ask for?

In fall 2000, I taught a master's level course in ML to about 25 students at New York University. Fortunately both for me and my students, I was able to use and assign excellent recent textbooks in the area: "Machine Learning" by Tom Mitchell and "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations" by Ian H. Witten and Eibe Frank. I recommend both books enthusiastically. A student who has mastered Mitchell has a solid grasp of the basic element of nearly every method of machine learning currently in use, and of almost every aspect of ML research. A student who has mastered Witten/Frank has a deep knowledge of the major ML techniques, and a strong sense of the opportunities and pitfalls to be encounted when these techniques are put into practice....

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.