Machine Learning
Machine Learning

Machine Learning
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McGraw-Hill College Division

UPC:
978007042807

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This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
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Machine Learning Specs:
Product NameMachine Learning
ManufacturerMcGraw-Hill College Division
Product Number MPN3345688
Retail Price $169.00
EAN-1409780070428072
UPC978007042807
Specifications 
TitleMachine Learning
ISBN0070428077
Author(s)Tom M. Mitchell
Release Date01 March, 1997, 1997-03-01
FormatHardcover
Num of Pages432
Num. of Items1
EAN9780070428072
Weight1.6 lbs.
Deal first added on:26-January-2004

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Computers Computer Science Computer Bks - General Information Artificial Intelligence - General Computer algorithms Algorithms (Computer Programming) Machine Learning
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Latest 6 Reviews
Here is what people are saying about the Machine Learning
4 Star Rating  "Good as an Introduction/ to get overview on ML"2009-04-24
- Reviewed By User: A2TZEOR6EY4DH7
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"
 
4 Star Rating  "Old But Still Good"2009-03-03
- Reviewed By User: A3P2U5ASAYZ90M
Given that this book was released in the mid 90's it should be outdated given the field that it is in, yet the book still provides a relevant overview of the major methods in the field of machine learning.
 
3 Star Rating  "A disapointment"2009-01-21
- Reviewed By User: A18DEM5BX5ZIW1
This book just breezes through all the machine learning concepts and does not go into any great details. It felt more like an index of learning techniques with a short summary on each.
 
4 Star Rating  "Please bow down to Tom Mitchell"2008-06-22
- Reviewed By michael_quinn
This is not my favorite machine learning book, but Tom Mitchell did us all a favor by writing it. It covers the breadth of topics that make up the machine learning discipline fairly completely. Since this book is about completely, there is also a shallowness, but that shallowness does not trim out complete descriptions of the algorithms covered. Oh no, all the gory math is there, what isn't there are simple examples.

My first time through the book, what gave me the biggest headache was trying to understand back propagation from the algorithm pseudo code and the proof of correctness. I really really wanted one simple example at that point to make sure I understood the correct use of all the greek terms.

So good book, but I really wanted "Machine Learning Examples" to go along with it back when I first picked it up. But once you understand, the book is a great reference.
 
5 Star Rating  "Good presentation of concepts"2008-05-31
- Reviewed By zac28506
The book machine learning by Mitchell provides a systematic overview of important concepts in the field. This is rather rare finding because most books present first of all algorithms but fall short communicating systematic insights that would help the reader to creatively develop methods by themselves.


It is needless to say that any book with the title 'machine learning' is inherent incomplete due to the incompletenss of the field itself. For this reason this book is not state of the art of current algrithms. Instead, again, concepts are at the center of focus.

Overall, well writen and a very good selection of examples and explanations. I recommend this to anyone for a general overview.
 
4 Star Rating  "Excellent Book, but for Academia Only"2008-04-15
- Reviewed By jonathan6885
This book is a redaction of many different white papers on the topic of machine learning. The material is very credible and accepted in the field, with very little (if any) temporal information (short term at least). With that said, it is also very dry and academic, and requires a solid background in mathematics to understand. Even if you are in the field, you're likely to read certain pages several times to embrace a concept... but once you embrace it, you will have some of the best foundational knowledge there is on the subject. If you're in the machine-learning field, you'll benefit from revisiting some of these subject, and probably learn a new thing or two.
 
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