Machine Learning with R
University of Utah
Registration: Use the following link to sign up for this workshop.
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General Information
What: This workshop provides a practical introduction to foundational machine learning algorithms, including linear regression, random forest, and XGBoost, with hands-on applications and best practices using tidymodels in R. Note that this workshop will NOT cover Large Language Models (LLMs) and while it may touch on Neural Network (NN)/Deep Learning models briefly at the end of the workshop, these will not be the focus.
Who: This workshop is designed for researchers, staff, and students who want to gain experience using machine learning techniques in practice in the R programming language.
Prerequisites: Participants should be familiar with the basics of the R programming language, including the tidyverse. Participants do not need to have any previous machine learning, statistical or mathematical experience to attend this workshop.
Requirements: Participants must bring a laptop onto which they can download R and Rstudio (and you should do so before the workshop).
Contact: Please email andrew.george@hsc.utah.edu and rebecca.barter@hsc.utah.edu for more information.
Resources
Posit Cloud
A Posit Cloud workspace will be set up prior to the workshop for those who cannot (or prefer not to) install applications on their laptop.
Download files and data
All relevant files will be provided here prior to the workshop.
Schedule
Note that the schedule below serves as a guideline. The start, end, and break times are fixed, but timing for each topics covered may vary as we may go faster or slower through the content.
Note that morning snacks and lunch will be provided on both days.
Day 1
| Time | Topic | Content |
|---|---|---|
| 9:00 | Introduction to Prediction Problems | slides, code |
| 9:45 | Linear Regression for Continuous Responses | slides, code |
| 10:30 | [Break] | |
| 10:45 | Evaluating Continuous Response Predictions | |
| 11:15 | Logistic Regression for Binary Responses | slides, code |
| 12:00 | [Lunch] | |
| 1:00 | Evaluating Binary Response Predictions | |
| 1:45 | Feature Engineering and Feature Selection | slides, code |
| 2:30 | [Break] | |
| 2:45 | Regularization with Lasso and Ridge | |
| 4:00 | [End] |
Day 2
| Time | Topic | Content |
|---|---|---|
| 9:00 | Decision Trees and Random Forest | slides, code |
| 10:30 | [Break] | |
| 10:45 | XGBoost | slides, code |
| 12:00 | [Lunch] | |
| 1:00 | Cross Validation and Parameter Tuning | slides, code |
| 2:30 | [Break] | |
| 2:45 | Class Imbalance | slides, code |
| 3:15 | Basic Neural Networks | slides, code |
| 4:00 | [End] |