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"Elements of Statistical Learning" should definitely be on the list. It is one of (if not THE) leading intro graduate textbooks in machine learning. http://ww
by dbecker 14y ago
"Elements of Statistical Learning" should definitely be on the list. It is one of (if not THE) leading intro graduate textbooks in machine learning.
http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/
or straight to the PDF at
http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_pri...
- gtani 14y agoAlso: http://www-nlp.stanford.edu/IR-book/ http://www-nlp.stanford.edu/IR-book/ https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf https://www.ics.uci.edu/~welling/teaching/273ASpring10/Intro... ____________ more info on Barber book mentioned in OP: https://news.ycombinator.com/item?id=4672930 https://news.ycombinator.com/item?id=4672930 http://www.reddit.com/r/MachineLearning/comments/i06a0/book_draft_introduction_to_machine_learning_by/ http://www.reddit.com/r/MachineLearning/comments/i06a0/book_...
- mikevm 14y agoThanks! Do you happen to have the solutions manual for the IR book?
- xaa 14y agoESL is a good book, but it should be mentioned that it is a significantly more difficult read than the other suggestions. Not at all an introductory-level text on ML.
- disgruntledphd2 14y agoWell, if you're willing to just follow the graphs and the argument the first time around, you can definitely get some use from it. I learned about classification, cross-validation and the bias-variance tradeoff from the first time I read it, and it significantly spurred me to deepen my understanding of the relevant mathematics.