MAT 314-01 - Topics in Applied Mathematics: Compressed Sensing

4 credits (Spring)

Compressed Sensing. An exploratory introduction to advanced numerical linear algebra and optimization techniques and their application to signal processing via compressed sensing. Through an inquiry-based approach, the course covers the theory and implementation of singular value decompositions, least squares solutions, function spaces, sparse representations, discrete orthogonal system, foundations of compressing sensing, restricted isometries, iterative algorithms, and convex optimization.

Prerequisite: MAT 218 or MAT 313.
Instructor: Blanchard