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Machine Learning 1st Edition

4.1 out of 5 stars 58 customer reviews
ISBN-13: 978-0070428072
ISBN-10: 0070428077
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Product Details

  • Series: Mcgraw-Hill Series in Computer Science
  • Hardcover: 432 pages
  • Publisher: McGraw-Hill Education; 1 edition (March 1, 1997)
  • Language: English
  • ISBN-10: 0070428077
  • ISBN-13: 978-0070428072
  • Product Dimensions: 6.3 x 1 x 9.6 inches
  • Shipping Weight: 2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (58 customer reviews)
  • Amazon Best Sellers Rank: #386,217 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover
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
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Format: Hardcover
It's pretty well done, it covers theory and core areas but - maybe it was more the state of the field when it was written - I found it unsatisfyingly un-synthesized, unconnected, and short of detail (but this is subjective). I found the 2nd edition of Russell and Norvig to be a better introduction where it covers the same topic, which it does for everything I can think of, except VC dimension.
The book sorely needs an update, it was written in 1997 and the field has moved fast. A comparison with Mitchell's current course (materials generously available online) shows that about 1/4 of the topics taught have arisen since the book was published; Boosting, Support Vector Machines and Hidden Markov Models to name the best-known. The book also does not cover statistical or data mining methods.
Despite the subjective complaint about lack of depth it does give the theoretical roots and many fundamental techniques decently and readably. For many purposes though it may have been superceded by R&N 2nd ed.
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Format: Hardcover
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.
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Format: Hardcover
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.
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By A Customer on January 25, 2003
Format: Hardcover
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.
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Format: Hardcover Verified Purchase
I bought this book because it was part of the required reading material for a class I am taking at Georgia Tech, Machine Learning. It is certainly not a beginners level book and requires a lot of math and statistics to make sense of it. I also was shocked at the price for this book considering how old it is and would have thought by now there would have been a revision but there hasn't been one. I may be critical of this book because I ended up dropping the class due to the fact I felt I lacked the background required to succeed and this book didn't help fill the gaps.
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Format: Hardcover Verified Purchase
This is extremely intuitive and general point of view on ML.
good for quick reading and getting introduced to the topic.
I'd recommend this to people starting ML.
then move on to more mathematically rigorous and specific books such as
"Pattern Classification"/ "Pattern Recognition and Machine Learning" / Hastie's "Element of Statistical Learning"

i never say this for a book. but it is too pricey for what it is offering.
FYI i think they should increase the price of Chris Bishop's book.
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