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There are so many textbook out there and the area of machine learning is heavy and immense. But, every once in a while, I have the chance to read a pedagogical book that deeply covers a great area in the field of machine learning. My personal favorites are :
Now, I'm in inquiring your personal experience in the hope of finding new books that are somewhat complementary to the previous list. Thanks, |
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In addition to Bishop, I would say the following ones for specific topics:
There is a great thread here on ML books, which I recommend you to check. |
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"The Elements of. Statistical Learning: Data Mining, Inference, and Prediction", Trevor Hastie, Robert Tibshirani, Jerome Friedman Second edition is downloadable on http://www-stat.stanford.edu/~tibs/ElemStatLearn/ 1
This book is good mainly for Supervised Learning and this book is not meant for beginners. Personally, I don't like this book, as its not so explanatory.
(Jan 30 '12 at 10:05)
Lancelot
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Pattern Classification by Richard O. Duda, Peter E. Hart, David G. Stork To whomever did it, I don't think downvoting is the proper way to express your disagreement here.
(Jan 30 '12 at 11:12)
levesque
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"Probabilistic Graphical Models" by Koller/Friedman (website). I particularly like these boxes, where they detail real world applications. Furthermore, it's well written and therefore a joy to read. The only disadvantages is the book's weight... not really readable away from a table. |
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"Neural Networks and Learning Machines" (3rd Edition) by Simon Haykin. It was of invaluable help in teaching some ML chapters. |
