Material Detail
Statistical Learning
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector...
Show MoreQuality
-
Peer Reviews
-
User Rating
- Comments (1) Comments
- Learning Exercises
- Bookmark Collections
- Course ePortfolios
- Accessibility Info
Jody Paul (Faculty)