Matrix Methods & Data Science

Catalog description

Topics in Linear Algebra drawn from a broad range of applications such as large-matrix computation techniques, pseudo-inverses and linear regression, non-negative matrices and the PageRank algorithm, low rank approximation and compressed sensing, computer graphics, multivariate Gaussian and weighted least squares, Markov chains, optimization, neural networks and machine learning.