MATH 7243 — Machine Learning and Statistical Learning Theory 1
4 semester hoursGraduateLectureusually offered: fall, springtypical days: M/WBostonTraditional
Introduces both the mathematical theory of learning and the implementation of modern machine-learning algorithms appropriate for data science. Modeling everything from social organization to financial predictions, machine-learning algorithms allow us to discover information about complex systems, even when the underlying probability distributions are unknown. Algorithms discussed include regression, decision trees, clustering, and dimensionality reduction. Offers students an opportunity to learn the implications of the mathematical choices underpinning the use of each algorithm, how the results can be interpreted in actionable ways, and how to apply their knowledge through the analysis of a variety of data sets and models.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 14 | 30 | 47% | 16.0 |
| Spring 2024 | 1 | 32 | 40 | 80% | 8.0 |
| Fall 2024 | 1 | 36 | 40 | 90% | 4.0 |
| Spring 2025 | 1 | 25 | 40 | 63% | 15.0 |
| Fall 2025 | 1 | 24 | 40 | 60% | 16.0 |
| Spring 2026 | 1 | 30 | 40 | 75% | 10.0 |
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 80% · T 0% · W 84% · Th 16% · F 20%
Common patterns: MW (65% of sections), WF (20% of sections), MR (16% of sections) — in patterns, R means Thursday
Professors
Fall
Spring
Percentages are each professor's average share of the season's enrolled students in recent terms.
Unlocks
Courses that list MATH 7243 in their prerequisites.
Links
Official catalog (MATH course descriptions) · Student reviews on RateMyHusky · All MATH courses · Plan it at numap.app