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Random matrix theory, algorithms and signal processing applications

Term: Fall 2010
Course No.: 598
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: Basic linear algebra and probability

Course Description:
This course covers and the theory and algorithms emerging from the study of random matrices as it is currently applied in signal processing, statistics and science. Topics include random sample covariance matrices, spectral limit theorems such as Wigner's semi-circle and Marcenko-Pastur laws, free probability, randomized numerical linear algebra, matrix statistics, passage to the continuum limit, moment methods, and compressed sensing. There will be a special focus on presenting the theory in a manner that facilitates the development of new applications and allows students that already have a topic in mind to to apply these ideas. Emerging applications in signal processing, wireless communications and statistical physics will be discussed.