MATH 6243 — Statistical Learning
4 semester hoursGraduateLectureusually offered: springtypical days: T/ThBostonTraditional
Presents fundamental principles of machine learning from a statistical standpoint. Focuses on supervised learning, including regression and classification methods. Topics include 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; and support-vector machines. Examines methods used to learn from data taking a deeper look at algorithms that may appear to be different but actually use similar principles. Discusses some unsupervised learning methods principal components and clustering. Uses a programming language to fit these models using modern techniques for visualization and reporting.
Prerequisites
- MATH 5010 (min C-)
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2026 | 1 | 21 | 30 | 70% | 9.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 100% · W 0% · Th 100% · F 0%
Common patterns: TR (100% of sections) — in patterns, R means Thursday
Professors
Spring
- Eric Gerber (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Unlocks
Courses that list MATH 6243 in their prerequisites.
Links
Official catalog (MATH course descriptions) · All MATH courses · Plan it at numap.app