EECE7345 (Big Data and Sparsity in Control, Machine Learning, and Optimization) course reviews and ratings at Northeastern (NEU). Compare professors with RateMyProfessor reviews.
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.
Data updated October 2026.