Introduction to Statistical Learning, with Applications in R
This post has a critical side.  Some readers will be offended.  But most readers should be glad that they got some hype peeled back and learned a little more.<p>Sounds like an updated course in otherwise just mostly classic multivariate statistics.  I have about four feet of such on my bookshelf.<p>Okay, have some more recent versions of regression and Breiman&#x27;s work on classification and regression trees and random forests and some resampling.  Right.<p>Okay.  Fine.  Maybe as good as multivariate statistics has been for decades, maybe a little better.  Fine.  They are using R instead of, say, SPSS,  SAS, or the old IBM SSL?  Okay.<p>Been doing that stuff since early in my career, have published in essentially multi-variate resampling, and am currently using some derivations I did for a modified, generalized version of some of that material.  Fine.<p>All that said, where did this stuff about <i>learning</i> enter?  This is nearly all old stuff;  where&#x27;d this <i>learning</i> stuff come from?<p>Getting estimates of parameters?  Sure.  Estimates that are minimum variance and unbiased?  If can, sure -- good stuff.  Confidence intervals on the estimates?  Of course.  Statistical hypothesis tests of <i>significance</i>  -- standard stuff.  A variance-covariance matrix, symmetric positive definite?  Sure.  Polar decomposition, non-negative eigenvalues, a basis of orthogonal eigenvectors, singular value decomposition, matrix condition number, ..., definitely.<p>But <i>learning</i>?  Where&#x27;d that come from?<p><i>Learning</i> sounds like an attempt, like <i>neural</i> networks, artificial <i>intelligence</i>, <i>deep learning</i> to suggest that this work is somehow close to human thinking, which, Virginia, I have to guarantee you it definitely is not.<p>They didn&#x27;t get <i>learning</i>
from statistics.  Okay, maybe they got <i>learning</i> from computer science.  So sounds like statistics just stole <i>learning</i> from computer science or computer science stole <i>learning</i> from psychology and stole all the rest from statistics.  Academic theft?  Old wine in new bottles with a new label, &quot;Learning&quot;?  Add mud to the waters, stir rapidly and confuse people? Hype?  Sell books?  Get students?  Get consulting gigs?  Get grants?  Get publicity?<p>Doesn&#x27;t look good.<p>Students trying to learn:  For the classic multivariate statistics, there are stacks of highly polished texts.  For more on closely related statistics, look at the work of Leo Breiman.  For hypothesis testing, that is nearly all classic Statistics 101.  For resampling, look at the work of P. Diaconis at Stanford or B. Efron at Yale.  In that way, you will have mostly highly polished, classic sources. Econometrics has made a lot of use of this material.  Also look at analysis of variance, which is closely related and quite powerful and heavily used in some fields of experimental science, e.g., agriculture.  For &quot;learning&quot;, mostly f&#x27;get about it.