Session Chair: Rachel Sippy, University of Florida
Machine learning (ML) is a popular approach for prediction of outcomes, including forecasting and spatial predictions. It is well-suited to large datasets with many potential predictor variables and has been applied to many problems in public health and healthcare. This workshop is intended for participants with some statistical modeling background, interested in using ML for prediction. In this hands-on workshop, you will learn to identify appropriate questions for ML, the principles of ML, and how it relates to other modeling approaches. We will apply ML methods with a sample dataset, understand the tools available for using ML, and other resources for ML. This workshop assumes a working knowledge of R, and a laptop with R and RStudio installed will be required for the workshop.