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236862Sparse and Redundant Representations and their Applications in Signal and Image Processing
Winter 2021/2022

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 ***