This course focuses 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. The coursed has a unique format as it combines A MOOC (via EdX) and followup flipped-classrom meetings in class.
*** As oposed to previous years, this version of the course will not include a final project, but rather a final exam ***
A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorization, 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.