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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 having 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
1pm-4pm each day:
• Part 1: Wednesday, February 22
• Part 2: Wednesday, March 1
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.
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: email@example.com