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My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077 http://www.a
by dude_abides 14y ago
My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077 http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007...
The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/102-4351241-7974535?_encoding=UTF8&v=glance&n=283155 http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
- exg 14y agoThe Elements of Statistical Learning, by T. Hastie, R. Tibshirani and J. Friedman [1] is also a very good one. Plus, the book is freely available on the authors' website. [1] http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/
- manaskarekar 14y agoI have been putting off buying that book because of the price, maybe I should check the library.
- achompas 14y agoSo this is a common misconception about the text (and Prof. Mohri's NYU class). In this case, "foundations" does not mean this is an introductory course. Rather, the class and text provide mathematical foundations for understanding the error bounds and growth complexity of various learning algorithms. So you'll be workin with convex optimization, reproducing kernel Hilbert spaces, and Rademacher complexity--definitely not "introductory" in the least! It's a completely different beast from Mitchell, Bishop, or EoSL (which I'm studying right now!), so I'm not sure comparisons are valid. It also fills a prominent gap in the ideas reviewed by the popular ML texts.