This course focuses on the conceptual understanding of a core set of practical and effective statistical methods for modeling and analyzing complex data, and applies them to solve real world problems. Topics include linear and logistic regression, linear models for classification, deep learning and neural networks, support vector machines and kernel methods, unsupervised methods, classification trees, boosting, and random forests.
Prerequisites
MAT 250, MAT 340