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Course Offerings

Course Evaluation Results

Course Details

Spring 2016-2017
ELE 538B   Graded A-F, P/D/F, Audit

Special Topics in Information Sciences and Systems - Sparsity, Structure and Inference

Yuxin Chen

This is a graduate level course covering various aspects of sparsity --- or more broadly, low dimensional structures --- that arise in large-scale data science applications. We introduce a mathematical theory for sparse representation, and cover several fundamental estimation/inference problems that are built upon sparse modeling, including sparse linear regression, compressed sensing, matrix completion, spectral estimation, etc. We focus on designing algorithms that are effective in both theory and practice.


Requirements/Grading:
Papers - 50%
Problem set(s) - 30%
Other (See Instructor) - 20%

Other Requirements:
Not Open to Freshmen.

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
42803 L01 9:30 am - 10:50 am M W   Friend Center   003   Enrolled:17 Limit:28