MATH 8333 Advanced Statistical Learning
This course is a survey of topics on fundamental theory and applications of statistical methods for supervised and unsupervised learning, including multiple linear and logistic regression models, discriminant analysis, regression splines, generalized additive models, model selection and regularization methods (ridge and lasso), tree-based methods, random forests, bagging and boosting, support vector machines, artificial neural networks techniques, principal component analysis, factor analysis, k-means, and hierarchical clustering.
Prerequisite
Consent of Instructor.
Corequisite
Consent of Instructor.