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Machine Learning [Hardcover]

Tom M. Mitchell
4.3 out of 5 stars  See all reviews (47 customer reviews)

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Book Description

March 1, 1997 0070428077 978-0070428072 1
This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.

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Machine Learning + Pattern Recognition and Machine Learning (Information Science and Statistics) + The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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Product Details

  • Series: McGraw-Hill Series in Computer Science
  • Hardcover: 432 pages
  • Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (March 1, 1997)
  • Language: English
  • ISBN-10: 0070428077
  • ISBN-13: 978-0070428072
  • Product Dimensions: 0.8 x 6.5 x 9.3 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (47 customer reviews)
  • Amazon Best Sellers Rank: #173,297 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
77 of 80 people found the following review helpful
5.0 out of 5 stars An excellent overview for the adv. undergrad or beg. grad September 30, 2002
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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55 of 59 people found the following review helpful
3.0 out of 5 stars Venerable, in both senses April 4, 2004
By eldil
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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21 of 22 people found the following review helpful
By A Customer
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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45 of 53 people found the following review helpful
2.0 out of 5 stars Covers important aspects but lacks depth September 8, 2001
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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9 of 9 people found the following review helpful
5.0 out of 5 stars Only book of it's kind January 25, 2003
By A Customer
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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5 of 5 people found the following review helpful
3.0 out of 5 stars Good as an Introduction/ to get overview on ML April 24, 2009
By S. Lee
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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23 of 30 people found the following review helpful
5.0 out of 5 stars Great compilation May 18, 2001
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?
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4 of 4 people found the following review helpful
5.0 out of 5 stars Excellent book, concise and readable June 21, 2006
This is a great book if you're starting out with machine learning. It's rare to come across a book like this that is very well written and has technical depth. The writing is to the point, maybe even a bit terse, but all that you need to know is in there. It's a bit old so doesn't cover kernel methods or SVM's but is still a great first machine learning book.
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Most Recent Customer Reviews
4.0 out of 5 stars Good but dated
I'm not an expert, but I think the book is a bit dated. It is interesting to flip through. It has some serious analysis of ideas with at least some mathematical justification. Read more
Published 20 days ago by Adamg
4.0 out of 5 stars Great Overview Book, Spine cracks...
This book is really good for an introduction to all types of machine learning algorithms. It has good detail for most of the algorithms. Read more
Published 6 months ago by emoKid
4.0 out of 5 stars Excellent, but the price?
But for the excessive price, I would have given this text five stars.

Another excellent text on machine learning is Pattern Recognition and Machine Learning - twice as... Read more
Published 6 months ago by Rafael Espericueta
5.0 out of 5 stars The book is an excellent guide for any student who has began to learn...
Machine Learning by Tom M. Mitchell is the first text book for students who aspire to learn the subject of machine learning. Read more
Published 9 months ago by Psyops
5.0 out of 5 stars Well written
I have read the first chapter and it seems like a good book. It doesn't have typos or annoying syntactical errors, so I don't have to try to figure out what the author intended... Read more
Published 18 months ago by bean
5.0 out of 5 stars This my favorite book.
I thank you a lot.
It was a perfect delivery.
This book is one of the best book in machine learning world.
I repect Tom M. Mitchell.
Published 18 months ago by Kim Hyoung Rae
4.0 out of 5 stars machine learning
The book is in good condition. the cover are good state. similarly, the content of the book. The editorial is doing excellent work
Published 20 months ago by Julio Olaya
5.0 out of 5 stars Very easy to read.
This is a well written and laid out book. It was a great resource in my graduate level AI class. If there was anything I didn't quite understand during lecture I was able to look... Read more
Published on July 2, 2012 by Christopher L Jackson
5.0 out of 5 stars Fast speed
I received the book in three days, much earlier than I expected. The condition of the book is also very good.
Published on September 4, 2011 by scotland
4.0 out of 5 stars The machine learning book
I am aware of no better introduction to machine learning than this book. Written by a leading authority in the field, it covers a huge range of important ML methods and ideas in a... Read more
Published on March 10, 2010 by PO8
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