The class teaches statistical and computational methods in machine learning with a focus on supervised/unsupervised learning. Students will gain experience implementing models to solve problems with data. Topics include ridge/lasso regression, support vector machines, kernel methods, conditional probability models, tree methods, gradient boosting, neural networks and mixture models.