COURSE DESCRIPTION

Reviews linear algebra with applications to life sciences, finance, and big data. Covers singular value decomposition, weighted least squares, signal and image processing, principal component analysis, covariance and correlation matrices, directed and undirected graphs, matrix factorizations, neural nets, machine learning, and hidden Markov models.

Prerequisites: 18.06

Text Book: Introduction to Linear Algebra, by Gilbert Strang