When I was in grad school it was called statistics. A few years later I told people I did machine learning and after seeing the confused look on their face I changed that to data science which excited them. More years passed, and without changing anything I do, I now practice AI, which seems scary to some people and somehow involves ML. During this talk we will demystify buzzwords, technical terms and overarching ideas. We'll touch upon key concepts and see a little bit of code in action to get a sense of what is happening in ML, AI or whatever else we want to call the field.

Modern statistics has become almost synonymous with machine learning, a collection of techniques that utilize today's incredible computing power. This two-part course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theory behind the curtain. We start with the foundation of it all, the linear model. We look how to assess model quality with traditional measures and cross-validation and visualize models with coefficient plots. Next we turn to penalized regression with the Elastic Net. After that we turn to Boosted Decision Trees utilizing xgboost. Along the way we learn modern techniques for preprocessing data.

Modern statistics has become almost synonymous with machine learning - a collection of techniques that utilize today's incredible computing power. Jared Lander walks you through the available methods for implementing machine learning algorithms in R and explores underlying theories such as the elastic net and boosted trees.

• Building the design matrix
• Penalized regression with the lasso and ridge methods
• Fitting models with glmnet
• Interactive visualization of the coefficient path
• Use cross-validation to choose the optimal lambda
• Visualize coefficients with coefplot
• Perform binary classification with a single tree with xgboost
• Train a boosted tree
• Tune xgboost hyperparameters
• Use validation data to understand performance
• Visualize variable importance
• Train a boosted random forest with xgboost

There is a lot of buzz around computing on GPUs, particularly for training deep learning models though information about implementation is often sparse. In this talk, we provide an overview of some of the best models, such as penalized regression, boosted trees and deep neural networks. After covering some of the math behind the models we demonstrate code, mostly in R, the language of choice for data scientists. Our goal is to show how simple it is to fit these models and that training them on GPUs takes marginally more effort than training on CPUs. By the end of the talk, you should have a strong sense of how to fit models with GPUs in R.