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Lecture 7

Page history last edited by mike@mbowles.com 9 years, 10 months ago

Here's a reprise on maximum likelihood. maximumLikelihood-reprise.ppt


As background for linear regression, we'll talk about bias versus variance errors and regularization.  Here are the figures from "Elements of Statistical Learning" by Hastie, Tibshirani and Friedman.  figures2.pdf


Here are some rough notes on coefficient shrinkage methods. Glmnet.pdf



This lecture principally covers glmnet.  The basic paper is http://www.jstatsoft.org/v33/i01/ by Professor Jerry Friedman at Stanford. 







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