STA 3241 - Statistical Learning

Credits: 3

Course Description: This is an introductory-level course in supervised learning. Topics include classification and regression, cross-validation and bootstrap, model selection, dimension reduction, tree-based methods, random forests and boosting, support-vector machines, principal components, and cluster analysis. Students will have hands-on experience in model building, machine learning, and implementation.
Prerequisites: STA 3036 - Probability and Statistics for Business, Data Science, and Economics or (MAS 3114 - Computational Linear Algebra and STA 2023 - Statistics 1 )