Data Mining and Machine Learning

4.0 credits · Graduate

We start off with a detailed refresher for Linear Regression. We then turn to popular methods for classification including Logistic Regression and Discriminant Analysis. Finally, we consider more advanced topics which may include - depending on the audience - Resampling Methods, Tree-based Methods, or Support Vector Machines. The statistics software R is introduced and used for applications.