R Machine Learning with tidymodels: Parts 1-2

February 27, 2024, 10:00am to February 29, 2024, 12:00pm

To receive a Zoom link after registering above, please fill out the affiliations form if you have not done so at least once before: https://dlab.berkeley.edu/affiliations

Trying to register, but not affiliated with the UCB campus? If you are from Berkeley Lab (LBL), CZ Biohub, or other organizations, please register via our partner portals here.

Location: Remote via Zoom. Link will be sent on the morning of the event.

Recordings: This D-Lab workshop will be recorded and made available to UC Berkeley participants for a limited time. Your registration for the event indicates your consent to have any images, comments, and chat messages included as part of the video recording materials that are made available.

Date & Time: This workshop is a 2-part series running from 10am-12pm each day:

• Tuesday, February 27
• Thursday, February 29

Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.


This two-part workshop provides an introduction to machine learning algorithms using the tidymodels package. It covers what machine learning is, which problems it is most and least equipped to address, and explores the tidymodels framework to fit supervised machine learning models in R.

Addressing machine learning problems requires a deep conceptual understanding of the material. While the workshop will cover coding in R, it will also dedicate a significant portion of the time to motivating machine learning techniques.

By the end of the workshop, learners should feel prepared to explore machine learning approaches for their own data problems. This workshop does not cover unsupervised machine learning techniques.

Prerequisites: Familiarity with R programming and data wrangling is assumed. If you are not familiar with the materials in Data Wrangling and Manipulation in R, we recommend attending that workshop first. In addition, this workshop focuses on how to implement machine-learning approaches. Learners will likely benefit from previous exposure to statistics.

Workshop Materials: https://github.com/dlab-berkeley/R-Machine-Learning

Software Requirements: Installation Instructions for getting started with using R and RStudio.

Feedback: After completing the workshop, please provide us feedback using this form

Questions? Email: dlab-frontdesk@berkeley.edu