algorithms

I got this book from the university's library, because I wanted a nice book that can show me different methods for machine learning, so I can learn the buzzwords, and understand their meaning, at least in principle. It was important to me that the book won't go too much in depth into any subject, and more importantly, that the book won't use unfamiliar terminology, unless explained before.

This book is indeed a nice overview of the field at the time it was written, although a lot happened in Machine Learning since, the book remains a good source for learning what can be done and how.

Reading the book is quite easy (I'm a graduate student in computer science), and quickly I got what I was asking for.

This book is not a magic pill for any problem. This is not a cookbook. So the compliments are in place as long as you know what you're looking for.

These days I attend a seminar where the students are asked, each student in turn to present one chapter from this book (and some other books). Surely, once the seminar is over, we (the students) will know enough to be able to chat about Machine Learning, and more importantly, we will know where to look for deeper texts if we wanted to.

I teach AI at the graduate level in a major US research University, and I specialize in the area. The book does cover many different areas of Machine Learning. Unfortunately, the treatment is quite superficial. A student would find it extremely difficult to grasp imortant concepts without referring to other material. It may be a good reference, but I would definitely not recommend it as the main textbook. Unfortunately, there seem to be very few books in this area adequate for a senior or graduate level course.

just the right content and texr easy to read!