Current Topics:

Introduction to machine learning using R programming language The purpose of the course is to provide students with knowledge of the basic assumptions, applications, interpretation, and fine-tuning of a set of commonly used machine language (ML) methods. These methods include types of regression (ridge, lasso, elastic net), PCA/SVD, CART & Random Forest, Naïve Bayes Classification, SVM, clustering algorithms, and simple feed forward neural networks. The course will begin with an overview of linear algebra used in ML. An introduction to text mining will be included. The primary programming language used in the course will be R but some examples of Python for ML will be included. Students will use R and Python to apply the ML methods to datasets. Prerequisite: Complete a masters level statistics course with a grade of B+ or better.






Spring 2021

Jeffrey Berg
R: 4:00 PM - 5:50 PM CANT 200
Paul Bailo
M: 4:20 PM - 6:10 PM ONLI
Paul Squires
R: 4:00 PM - 5:50 PM CANT 200