Introduction to Statistical Learning (Summer)

New techniques have emerged for both predictive and descriptive learning that help us make sense of vast and complex data sets. The particular focus of this course will be on regression and classification methods as tools for facilitating machine learning. This math-light course is only offered remotely via video segments and TAs will host remote weekly office hours using an online platform such as Zoom. Computing will be done in R.

Note: This course is offered remotely only via video segments (MOOC style). Lectures will not be recorded on-campus.

Topics Include

• Introduction to supervised learning
• Linear and polynomial regression
• Cross-validation and the bootstrap
• Model selection and regularization methods
• Tree-based methods, random forests and boosting
• Support-vector machines
• Nonlinear methods, splines and generalized additive models
• Principal components and clustering