MATH 7339 — Machine Learning and Statistical Learning Theory 2
4 semester hoursGraduateLectureusually offered: fall, springtypical days: M/WBostonTraditional
Continues MATH 7243. Further covers theory and methods for regression and classification, along with more advanced topics in machine learning, statistical learning, and deep learning. Reviews the basics of machine learning in a broader and deeper way. Additional topics are drawn from smoothing methods, clustering, latent variable models, mixture models, Markov decision process and reinforcement learning, and neural networks. Discusses recent research papers on image classification and segmentation, generative adversarial network, neural style transfer, natural language processing, and topological data analysis. Uses theory, models, and algorithms to analyze a variety of datasets.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 10 | 25 | 40% | 15.0 |
| Spring 2024 | 1 | 23 | 25 | 92% | 2.0 |
| Fall 2024 | 1 | 18 | 25 | 72% | 7.0 |
| Spring 2025 | 1 | 17 | 25 | 68% | 8.0 |
| Fall 2025 | 1 | 19 | 30 | 63% | 11.0 |
| Spring 2026 | 1 | 7 | 30 | 23% | 23.0 |
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 93% · T 7% · W 62% · Th 38% · F 0%
Common patterns: MW (62% of sections), MR (31% of sections), TR (7% 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 7339 in their prerequisites.
Links
Official catalog (MATH course descriptions) · Student reviews on RateMyHusky · All MATH courses · Plan it at numap.app