Optimization and Computational Linear Algebra

DSGA 1014, NYU Center for Data Science, 2020

Description

Despite the name, this class is an proof-based linear algebra class taught at the graduate level. We emphasize fundamental linear algebra (rank-nullity theorem, eigenvalues, SVD), and applications of linear algebra in data science, including PCA, linear regression, and gradient descent. Previous experience with basic linear algebra at the undergraduate level is assumed.
Useful Links:
Class website
Class github
My recitation materials