EECE 7345 — Big Data and Sparsity in Control, Machine Learning, and Optimization
4 semester hoursGraduateLecturetypical days: TBostonTraditional
Covers the issue of handling large data sets and sparsity priors, presenting very recently developed techniques that exploit a deep connection to semi-algebraic geometry, rank minimization, and matrix completion. Focuses on applications, including control and filter design subject to information flow constraints, subspace clustering and classification on Riemannian manifolds, and activity recognition and classification and anomaly detection from video sequences. The goal of this course is to introduce the subject to people in the systems, machine-learning, and computer vision communities faced with “big data” and scaling problems and serve as a quick reference guide, summarizing the state of the art as of today and providing a comprehensive set of references.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2024 | 1 | 21 | 36 | 58% | 15.0 |
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 0% · F 0%
Common patterns: T (100% of sections) — in patterns, R means Thursday
Professors
Fall
- Mario Sznaier (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (EECE course descriptions) · Student reviews on RateMyHusky · All EECE courses · Plan it at numap.app