MATH 6333 Statistical Learning

This course introduces the statistical methods for supervised and unsupervised learning, including topics of regression and classification, such as linear regression, multiple regression, logistic regression, K-nearest neighbors, polynomial regression, splines regression, tree regression, random forests, ridge regression and the Lasso, linear and quadratic discriminant analysis, support vector machines, artificial neural networks regularization techniques, and boosting techniques. During the course, we will apply these techniques in several case studies.

Credits

3

Prerequisite

Consent of instructor

Schedule Type

Lecture

Grading Basis

Standard Letter (A-F)

Administrative Unit

School of Mathematical & Stat.

Offered

As scheduled