Adam Loy (Carleton College), Jaime Davila (St. Olaf College)
Abstract
Statistical learning, also known as machine learning, is commonly used across many disciplines. However, existing materials for teaching statistical learning reduce the creation of models to a prescriptive recipe and do not encourage students to use a hands-on, active learning approach. As a consequence of this gap in educational materials, practitioners use statistical learning models without a clear understanding of how they work and their limitations, resulting in the creation of erroneous models. We have created active learning materials that introduce students to the principles of statistical and machine learning by using the R programming language and the tidymodels framework. This workshop will introduce attendees to a few commonly used statistical learning models appropriate for an undergraduate audience, the tidymodels framework, and our active-learning approach.
By the end of the workshop, participants will:
- Understand the general design principles of the tidymodels ecosystem.
- Practice using tidymodels to fit common statistical learning models.
- Experience an active-learning approach to statistical learning.
Prerequisites:
Knowledge of linear regression and a basic understanding of R syntax and tidyverse framework
Technology:
Attendees should bring a laptop with R and RStudio installed. We will also send out instructions on how to install the necessary R packages before the workshop.