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Any time spent in this way, even if it ends without success, will make our solutions ∗ †Įasier to understand and more memorable. It is therefore our strong recommendation that readers of the book should not look at our responses to any of the exercises before making a substantial effort to understand it without this aid. Moreover, material understood only at a shallow level is easily forgotten. It is all too common to read through some material, convince oneself that one understands it well, but then find oneself at sea when trying to apply it in even a slightly different situation. Now it is well-known in all branches of learning, but in particular in mathematical learning, that the way to learn is to do, rather than to read. A valuable by-product of writing up mathematical material, is that often one finds gaps and errors in what one has written. Gaining understanding is time-consuming and intellectually demanding, so it seemed sensible to record our efforts in LaTeX, and make it available on the web to other readers. For various reasons, both authors of these notes felt the need to understand the book well, and therefore to produce notes on the text when we found the text difficult at first reading, and answers to the exercises. It is also very challenging, particularly if one faces it without the support of teachers who are expert in the subject matter. It is a standard recommended text in many graduate courses on these topics. Introduction The Elements of Statistical Learning is an influential and widely studied book in the fields of machine learning, statistical inference, and pattern recognition.
An introduction to statistical learning solutions manual#
A Solution Manual and Notes for: The Elements of Statistical Learning by Jerome Friedman, Trevor Hastie, and Robert Tibshirani John L.