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 we want to help 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 will receive 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.
Please use the drop-down menu above to access course lecture schedule, notes, and homeworks. The course notes section is password protected (for pre-publishing reasons), if you would like a copy please shoot me an email at firstname.lastname@example.org and I’ll send one your way!