AMS 580, Statistical Learning

This course teaches the following fundamental topics: (1) General and Generalized Linear Models; (2) Basics of Multivariate Statistical Analysis including dimension reduction methods, and multivariate regression analysis; (3) Supervised and unsupervised statistical learning.

Spring Semester
3 credits, ABCF grading

This course will first review classical linear and generalized linear models such as Linear Regression, Logistic Regression, and Linear Discriminant Analysis.  We shall then study modern Resampling Methods such as Bootstrapping, and modern variable selection methods such as the Shrinkage Method. We will study traditional multivariate analysis methods including cluster analysis, principal component analysis, and multivariate regression methods such as structural equation modeling.  Finally, we shall introduce modern non-linear statistical learning methods such as the Generalized Additive Models, Decision Trees, Random Forest, Boosting, Bagging, Support Vector Machines, and Neural Networks.

Required Textbooks:

"An Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani; (Springer Texts in Statistics) 1st edition, 2013; Corr. 7th printing 2017 edition; ISBN: 978-1461471370

"The Elements of Statistical Learning:  Data Mining, Inference, and Prediction",  by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2nd edition (2018),  (Part of Springer Series of Statistics);  Springer Publishing; ISBN: 978-0387848587 (e-Book)