A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorizationד, Least-Squares, Gram-Schmidt algorithms and QR decomposition, eigenvalues and SVD, iterative methods for solving linear systems of equations, iterative methods for LS, iterative methods for finding eigenvalues and eigen-vectors, numerical errors and their effect, and an introduction to the discrete Fourier analysis via Circulant matrices.
A MOOC (via EdX) course on sparse representations and their uses in signal and image processing and machine learning. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), applications in image processing (denoising, inpainting, deblurring, compression), and connection to machine learning topics.