as of 02/07/23 08:02AM
| Course Section Number: | MATH-5553-01 |
| Title: | Statistical Learning |
| Status: | Open |
| Block: | |
| Instructional Method: | Lectures |
| Course Description: | Statistical methods in supervised and unsupervised learning including classification and clustering, regularization and shrinkage for high dimensional data sets, non-linear models. Applications using these methods will be explored. |
| Requisite Courses: | Prequisites: STAT 4813 and MATH 4123 or equivalents and permission of instructor. |
| Comments: | Instructor Permission Required |
| Meeting Times: | KEP 3160 MW 03:30PM-04:45PM |
| Instructor: | Redner R |
| Books and Supplies |
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| Book Title: | The Elements of Statistical Learning: Data Mining, Inference and Prediction |
| Author: | Trevor Hastie, Robert Tibshirani, Jerome Friedman |
| Publisher: | Springer |
| Edition: | 2nd |
| ISBN: | 9780387848570 |
| Publishers Suggested Retail Price: | $89.95 |
| Required: | Required |
| Comments: | Free electronic copy is available at https://web.stanford.edu/~hastie/ElemStatLearn/ and https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf |
| Book Title: | An Introduction to Statistical Learning with Applications in R |
| Author: | James, Witten, Hastie and Tibshirani |
| Publisher: | On the web |
| Edition: | 2nd |
| ISBN: | NO_ISBN |
| Publishers Suggested Retail Price: | |
| Required: | Required |
| Comments: | Free Electronic Copy On the Web |