Elements of Statistical Learning is Hastie's book. Between it and Bishop's book (i.e. Pattern Recognition and Machine Learning), I prefer Hastie's for clarity o

Elements of Statistical Learning is Hastie's book. Between it and Bishop's book (i.e. Pattern Recognition and Machine Learning), I prefer Hastie's for clarity of exposition. In particular, I find that ESL better conveys the sort of intuitive understanding of _why_ a method works that facilitates practical applications and extensions to novel contexts. Though, there are topics for which the increased equations/explanations ratio of Bishop's book is useful. I've TAed my university's graduate ML course for the past couple of years, so I've read most chapters of these books in some detail and have hands-on experience using them to help people who are looking closely at these topics for the first time. Interestingly, SVMs are actually a good example of when I'd suggest both books.