- 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)
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Machine Learning 1st Edition
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Top Customer Reviews
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
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 For many purposes though it may have been superceded by R&N 2nd ed.
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.
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.
Most Recent Customer Reviews
The definitive text on Machine Learning. Fully met my expectations for information about machine learning.Published 4 months ago by Chuck Cottrill
Solid and accessible intro to the topic. Might be a bit dated. Could be more concise, but readers without a strong math background might appreciate the lengthy examples and... Read morePublished 8 months ago by Amazon Customer
In 2015, there is no audience for whom this book is the best option. For a graduate student, it's too brief and now also too dated. Read morePublished 9 months ago by Terran
Presents the ideas of machine learning in a clear and easy to understand way while showing some basic examples of when to use different strategies.Published 12 months ago by Nickolas Evans
The hard cover link is for Machine Learning by Tom M. Mitchell.
The paperback is a different book called Machine Learning: A Guide to Current Research also by Tom M. Mitchell.
This is a good text that I have as a companion to my machine learning course. I will say that it could use an update for content and style especially as concerns NLP.Published 19 months ago by Amazon Customer
A good book. It's rather dry reading, but seems to be a good overview of machine learning. I've only used it for supervised learning so far in my grad ML class.Published 22 months ago by John Clem