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AMS 380, Data Mining

Catalog Description: This course will teach the basic ingredients of classical and contemporary statistical data mining methods including dimension reduction, variable selection, pattern recognition, and predictive modeling using traditional general linear models and generalized linear models, and modern statistical learning methods such as classification and regression tree, random forest, neural networks, etc.  We will also teach how to run these procedures with the statistical programming language R.

Prerequisite: AMS 311

3 credits

Offered initially spring 2021; thereafter, spring, summer and fall.



Course Materials for Spring,Summer, and Fall 2021:

"Data Mining with R:  Learning with Case Studies" by Luis Torgo; 2nd edition (Chapman & Hall / CRC Data Mining and Knowledge Discovery Series); 2017; ISBN: 978-148223489-3


Learning Outcomes for Data Mining:

Train students to learn the basic statistical data mining techniques; teach statistical programming language R