DSC 155. Hidden Data in Random Matrices (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/DSC.html#dsc155
Description
Rigorous treatment of principal component analysis, one of the most effective methods in finding signals amidst the noise of large data arrays. Topics include singular value decomposition for matrices, maximal likelihood estimation, least squares methods, unbiased estimators, random matrices, Wigner’s semicircle law, Markchenko-Pastur laws, universality of eigenvalue statistics, outliers, the BBP transition, applications to community detection, and stochastic block model. Prerequisites: MATH 180A and (MATH 18 or MATH 31AH). Students will not receive credit for both MATH 182 and DSC 155.
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No courses have DSC 155 as a prerequisite.