Course Syllabus

Professor: Dr. Brent Coull

Statistical learning is a collection of flexible tools and techniques for using data to construct prediction algorithms and perform exploratory analysis. This course will introduce students to the theory and application of methods for supervised learning (classification and regression) and unsupervised learning (dimension reduction and clustering). Students will learn the mathematical foundations underlying the methods, as well as how and when to apply different methods. Topics will include the bias-variance tradeoff, cross-validation, linear regression, logistic regression, KNN, LDA/QDA, variable selection, penalized regression, generalized additive models, CART and BART, random forests, gradient boosting, kernels, SVMs, PCA, K-means clustering, and kernel machine regression. Homework will involve mathematical and programming exercises, and exams will contain conceptual and mathematical problems. Programming in R will be used throughout the course to provide hands-on training and practical examples.