A Solution Manual and Notes for:
The Elements of Statisti cal Learning
by Jerome Friedman, Trevor Hastie,
and Robert Tibshirani
John L. Weatherwax
∗
Dav id Epstein
†
5 March 2018
Introduction
The Elements of Statistical Learning is an influential and widely studied b ook in the fields
of machine learning, statistical inference, and pattern recognition. It is a standar d recom-
mended text in many graduate courses on these topics. It is also very challenging, particularly
if one faces it without the support of teachers who are expert in the subject matter. For
various reasons, both authors of these no tes 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. Gaining understanding is time-consuming and intellectually
demanding, so it seemed sensible to record our efforts in LaTeX, and make it available o n
the web to ot her readers. A valuable by-product of writing up mathematical material, is
that often one finds gaps and errors in what one has written.
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. It is all too common to read through
some material, convince oneself that one understands it well, but t hen find oneself at sea
when trying to apply it in even a slightly different situation. Moreover, material understoo d
only at a shallow level is easily forgott en.
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. Any time spent in this way, even if it ends without success, will make our solutions
∗
wax@alum.mit.edu
†
David.Epstein@warwick.ac.uk
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