EECS 495: Sparse and Low-rank recovery problems in machine learning

Due to their wide applicability, sparse and low-rank models have quickly become some of the most important tools for today’s researchers in machine learning, statistics, optimization, bioinformatics, computer vision, as well as signal and image processing.  With this 495 topics class, last offered Winter quarter of 2013, we aimed to quickly bring interested students and researchers from this wide array of disciplines up to speed on the wide applicability of sparse and low-rank models. The ultimate aim of the course is to empower students by equiping them with all the modeling and optimization tools they’ll need in order to formulate and solve problems of interest using sparse and low-rank tools.

In addition to a well curated collection of reference materials, registered students received a draft of a forthcoming manuscript authored by the instructors on sparse and low rank models and algorithms to use as class notes.  For a complete class syllabus, including list of topics to be covered, please click here.

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