Semester Course: Statistical Learning


(April 15, 2014 9:10 AM - 11:15 AM)

Semester Course: Statistical Learning

Abstract

The course covers basic concepts of modern statistical learning theory. The theory itself is born out of the challenge of understanding vast amounts of data routinely collected in modern science and has led to the development of new tools in the field of statistics, as well as has spawned new computer-assisted areas of research, such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often described with different terminology. This course attempts to collect some main ideas of statistical learning into a common conceptual framework appropriate for the audience with mathematical background.

 

Date Topic Comments
Jan 21 Introduction and overview ELS2 Chap 1,2
Jan 28 Cancelled due to weather  
Feb 4 Computational methods for regression ELS2 Chap 3
Feb 18 Linear and kernel-based classification ELS2 Chap 4,6
Feb 25 Model assessment and selection ELS2 Chap 7
Mar 4 Tree based models and neural nets ELS2 Chap 9, 11
Mar 25   Data set discussion
Apr 1 No Class  
Apr 15 Random forests and ensemble learning ELS2 Chap 16
  High dimensional data ELS2 Chap 18

 

ELS2 - Elements of Statistical Learning, Second Edition by T. Hastie R. Tibshirani, and J. H. Friedman

Additional textbook: An Introduction to Statistical Learning with Applications in R, by James Witten, Hasite, and Tibshirani