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E9 203: Compressed Sensing and Sparse Signal Processing: Video Lectures and Notes

Lecture 1: Introduction to underdetermined linear systems, penalty functions, l1 minimization, and linear programming. Video, Notes-SVG, Notes-PDF.
Lecture 2: Best s-term approximation, and why lp-ball with p < 1 promotes sparsity. Video1, Video2,Notes-SVG, Notes-PDF.
Lecture 3: Tighter bounds on compressible signals, minimal number of measurements for unique sparse vector recovery. Video, Notes-SVG, Notes-PDF.
Lecture 4: Minimal number of measurements for the recovery of all s-sparse vectors. Video, Notes-SVG, Notes-PDF.
Lecture 5: Recovery of individual sparse vectors. NP-hardness of l0 minimization. Video, Notes-SVG, Notes-PDF.
Lecture 6: L1 minimization leads to sparse solutions. Video, Notes-SVG, Notes-PDF.
Lecture 7: The orthogonal matching pursuit algorithm. Video, Notes-SVG, Notes-PDF.