Schedule of topics for FOR 796: Machine Learning Concepts and Applications
| Week | Topic |
|---|---|
| 1 | Prediction, Estimation, and Attribution |
| 2 | Regression |
| 3 | Classification |
| 4 | Classification with imbalanced classes |
| 5 | Decision Trees |
| 6 | Random Forests |
| 7 | Hyperparameters and Model Tuning |
| 8 | Gradient Boosting Machines |
| 9 | Stochastic GBMs and Stacked Ensembles |
| 10 | k-Nearest Neighbors |
| 11 | Support Vector Machines (as time allows) |
| 12-13 | Project Work |
| 14 | Presentations |
Each week there will be a handout to read before class. You are encouraged to type the code from the handouts into your own R session, in order to develop familiarity with the syntax and develop muscle memory for common tasks.
Class time on Wednesday will be a discussion format, dedicated to answering any questions from the reading. Office hours will be available weekly to help debug code problems or answer more specific questions.
The primary assignment for this class is a single project where you’ll apply the techniques from this course to a data set of your choosing.
The last week of the semester you’ll hand in two files – one containing the code used in the course of your project, the other a short report about the question you set out to answer, the methods you used, your results, and some reflection on what went well and what you’d change to make your models better. You’ll also give a five-minute presentation on your project during the final class session.
A rubric for this project will be made available during the semester.