Teaching grades so far are found HERE (in Hebrew, where 1 stands for the lowest quality and 5 the highest, the left-most column is the overall grade).
A graduate course on sparse representations and their uses in signal and image processing. 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), and applications in image processing (denoising, inpainting, deblurring, compression).