This is a course about machine learning and econometrics for graduate students. Machine learning uses data to predict some variable as a function of other variables, and econometrics use statistical methods for prediction, inference, and causal modeling of economic relationships (Varian, 2014). In the past few decades, computer-based technology allows people to collect enormous data sets, orders of magnitude larger than those that classic statistical theory was designed to deal with. Huge data demands new methodology, and the demand is being met by a burst of innovative computer-based statistical algorithms in estimation and prediction. While algorithmic invention is a more free-wheeling and adventurous enterprise, inference is playing catch-up as it strives to assess the accuracy, good or bad, of some hot new algorithmic methodology. In other words, the inference / algorithm race is a tortoise-and-hare affair (Efron and Hastie, 2016).