MWF | 1:00 - 3:00 |
TTh | 9:00-12:00 |
or by appointment |
e-mail: bdeppa@winona.edu
An Introduction to Statistical Learning - James, Witten, Hastie, and Tibshirani (Springer-Verlag 2014). The book is available for free in PDF format at the textbook website: www.StatLearning.com.
More Advanced Text - Elements of Statistical Learning - Hastie and Tibshirani (Spring-Verlag). This book is also available for free in PDF format at the textbook website: https://web.stanford.edu/~hastie/ElemStatLearn/
Another Optional Text - Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Spring-Verlag 2013). This book is available through Amazon.com and here is the website for the text: appliedpredictivemodeling.com
PDF of the Applied Predictive Modeling text: click here
Grading:
Your course grade will be based entirely on your performance on course assignments/projects.
There may be both a midterm and final course project that will require you to present your
analyses to the class in a seminar format. This course does satisfy the
oral flag requirement as a result. It will be given no additional weight except for the fact that these projects will be worth a
large number of points relative to the other course assignments.
Course Projects:
All assignments will be posted on this webpage along with R commands and
additional help to get you started on them. You may work in groups of two on all assignments and make one assignment submission for your group. Make sure both group member names are at the top of the assignment. Assignment submissions will done through dropboxes on D2L. Group assignments are NOT a division of labor, i.e. both group members must be working collaboratively on all parts of the assignments/projects. Academic dishonesty will result in dire consequences and I will cry. (WSU policy)
Computing:
We will primarily be using R in this course and a large number of packages for R which you will have to install. However, we will be using
JMP on occasion.
I. Introduction - Supervised (regression & classification) and Unsupervised Learning |
II. Review of Regression Modeling - Review of the important concepts from STAT 360. Particular emphasis on residual plots, case diagnostics, transformations, and model selection. I will use R and content from STAT 360 in reviewing this material. |
III. Prediction Methods for a Numeric Response a. shrinkage methods (Ridge, LASSO, LARS) |
IV. Prediction Methods for a Categorical Response (Classification Methods) a. logistic regression |
V. Resampling Methods - (covered throughout the course) a. bootstrap and jackknife |