COURSE DESCRIPTION

Reviews linear algebra with applications to data science and computations: 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. Leading to final project not exams.

Prerequisites: 18.06 Linear Algebra

Textbook: Linear Algebra and Learning from Data, by Gilbert Strang (Chapters on Stellar, book 12/2018)

In the first class, Professor Strang will have advice about obtaining the new textbook Linear Algebra and Learning from Data