Abstract: Modern statistics has become almost synonymous with machine learning; a collection of techniques that utilize today's incredible computing power. This course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theories behind the curtain, covering the Elastic Net, Decision Trees and cross-validation. Attendees should have a good understanding of linear models and classification and should have R and RStudio installed, along with the `glmnet`, `rpart`, `rpart.plot`, `boot`, `ggplot2` and `coefplot` packages. Elastic Net: Learn about penalized regression with the Lasso and Ridge, Fit models with `glmnet`, Understand the coefficient path, View coefficients with `coefplot`. Decision Trees: Learn how to make classifications (and regression) using recursive partitioning, Fit models with `rpart.`, Make compelling visualizations with `rpart.plot`. Cross-Validation: Learn the reasoning and process behind cross-validation, Cross-validate glm models with `cv.glm`
Bio: Jared Lander is theChief Data scientist at Lander Analytics, Columbia Professor, Author of R for Everyone and Organizer of the World's Largest R Meetup. He is Author or Best seller R book called "R for Everyone"