Random matrices, theory and applications (opens 2018-2019)
The course will introduce students to the tools used to study the properties of eigenvalues and corresponding eigenvectors. Fundamental results such as the semi-circle law and Marcenko-Pastur distribution will be derived many times using different techniques. The goal of the course will be to understand the recent results in estimation of large noisy matrices and in particular of sample covariance matrices.
This course will focus on getting computationally useful results for large matrices found in real applications. The level of rigor will be that of physics.
(co-CEO Capital Funds Management)
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