ELE538B: Sparsity, Structure and InferenceYuxin Chen, Princeton University, Spring 2017
Course DescriptionThis is a graduate level course covering various aspects of sparsityâ€”or more broadly, lowdimensional structuresâ€”that arise in largescale data science and machine learning applications. We will introduce a mathematical theory for sparse representation, and will cover several fundamental inference/estimation/learning problems that are built upon sparse modeling, including sparse linear regression, graphical models, compressed sensing, matrix completion, robust principal component analysis, super resolution, etc. We will focus on designing algorithms — in particular, optimizationbased methods — that are effective in both theory and practice. Lectures
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