MATH 4570 — Matrix Methods in Data Analysis and Machine Learning
4 semester hoursUndergraduateLectureusually offered: fall, springtypical days: M/ThBostonTraditional
Introduces concepts and methods of linear algebra for understanding and creating machine learning and deep learning algorithms. Topics include various matrix factorizations, symmetric positive definite matrices, inner product spaces, matrix calculus, applications to probability and statistics, and optimization in high-dimensional spaces. Explores the mathematics behind data analysis, machine learning, and deep learning, including gradient descents, Newton's methods, principal components analysis, linear regression and linear methods in classification, neural networks, and convolutional neural networks. Offers students opportunities to learn and practice Python skills with labs and the final project.
Prerequisites
- MATH 2331 (min D-)
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 48 | 49 | 98% | 1.0 |
| Spring 2024 | 1 | 47 | 48 | 98% | 1.0 |
| Fall 2024 | 1 | 52 | 56 | 93% | 4.0 |
| Spring 2025 | 2 | 62 | 93 | 67% | 15.5 |
| Fall 2025 | 1 | 52 | 60 | 87% | 8.0 |
| Spring 2026 | 1 | 48 | 48 | 100% | 0.0 |
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 94% · T 6% · W 48% · Th 79% · F 0%
Common patterns: MR (47% of sections), MWR (32% of sections), MW (15% of sections), T (6% of sections) — in patterns, R means Thursday
Professors
Fall
- Lee-Peng Lee (100% of students) · reviews
Spring
- Lee-Peng Lee (70% of students) · reviews
- He Wang (30% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (MATH course descriptions) · Student reviews on RateMyHusky · All MATH courses · Plan it at numap.app