Trying to register, but not affiliated with the UCB campus? If you are from Berkeley Lab (LBL), UCSF, or CZ Biohub, please register via our partner portals here.
If you are from the UCB campus there's no more waitlist! But after registering above, please do fill out the affiliations form if you have not done so at least once before: https://dlab.berkeley.edu/affiliations
Location: Remote via Zoom. Link will be sent on the morning of the event.
Date & Time: This workshop is a 2-part series that runs from 9am-12pm
- Tuesday, March 1
- Thursday, March 3
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.
Description
In this workshop, we provide an introduction to machine learning algorithms by making use of the tidymodels package. First, we discuss what machine learning is, what problems it works well for, and what problems it might work less well for. Then, we'll explore the tidymodels framework to learn how to fit machine learning models in R. Finally, we will apply the tidymodels framework to explore multiple machine learning algorithms in R.
By the end of the workshop, learners should feel prepared to explore machine learning approaches for their data problems.
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.
Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed and basic familiarity with the tidyverse.
Workshop Materials: https://github.com/dlab-berkeley/R-Machine-Learning
Software Requirements: Installation Instructions for getting started with this working using R and RStudio.
Feedback: After completing the workshop, please provide us feedback using this form
Questions? Email: dlab-frontdesk@berkeley.edu