MATH 182Hidden Data in Random Matrices
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. Students will not receive credit for both MATH 182 and DSC 155. Completion of MATH 102 is encouraged but not required.
Prerequisites
From the catalog: “MATH 180A, and MATH 18 or MATH 31AH. Students who have not completed listed prerequisites may enroll with consent of instructor.”
Where it sits in degree plans
One of the options in (2)
- Artificial Intelligence
- Probability and Statistics (B.S.)