Applied Linear Algebra for Scientific Computing and Data Science.

Catalog description

This course will introduce foundational mathematical concepts for scientific computing and machine learning through the lens of linear algebra. Topics will include a review of highlights of linear algebra, with a focus on the singular value decomposition, elements of numerical linear algebra, including iterative methods and pseudospectra, and special matrices, like those that appear in the Fourier transform and fundamental signal processing algorithms, and algorithms that take advantage of matrix structure, like multigrid methods.