Introduction to Statistical Learning

Description

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.

Please note that is an online-only course, lectures will not be recorded on-campus.

Prerequisites

Introductory courses in statistics or probability (STATS60 or equivalent), linear algebra (MATH51 or equivalent), and computer programming (CS105 or equivalent).

A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.

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

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.