This is the repository for D-Lab’s Introduction to Machine Learning in R workshop.

RStudio Binder:

• Background on machine learning
  • Classification vs regression
  • Performance metrics
• Data preprocessing
  • Missing data
  • Train/test splits
• Algorithm walkthroughs
  • K-nearest neighbors
  • Decision trees
  • Random forests
  • Gradient boosted machines
  • SuperLearner ensembling

We assume that participants have familiarity with:

• basic R syntax
• statistical concepts such as mean and standard deviation

Please bring a laptop with the following:

• R version 3.5 or greater
• RStudio integrated development environment (IDE) is highly recommended but not required.

Browse resources listed on the D-Lab Machine Learning Working Group repository. Scroll down to see code examples in R and Python, books, courses at UC Berkeley, online classes, and other resources and groups to help you along your machine learning journey!