R Introduction to Machine Learning tidymodels

Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

We will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework.

Participant Technology Requirement: Laptop with long term STABLE version of QGIS installed, internet connection, Zoom account required

This workshop will be held in two parts – sign up for one or both sessions!

Part 1: Mon, April 19, 2021 – 1:00 PM to 4:00 PM

Part 2: Wed, April 21, 2021 – 1:00 PM to 4:00 PM