STAT 221 Statistical Machine Learning

Explores statistical methods in machine learning. Discusses the methodology and algorithms behind modern supervised and unsupervised learning techniques that are commonly applied to complex, high dimensional problems. Course topics include linear and logistic regression, classification, clustering, resampling methods, model selection and regularization, and non-linear regression. Students also gain exposure to popular statistical machine learning algorithms implemented in R. One focus is on understanding the formulation of statistical models and their implementation, and the practical application of learning methods to real-world datasets.


Prerequisite(s): STAT 205 or STAT 208 or by permission of instructor. Enrollment is restricted to graduate students.