Matrices and Vector Spaces

Catalog description

Solving systems of linear equations, matrix factorizations and fundamental vector subspaces, orthogonality, least squares problems, eigenvalues and eigenvectors, the singular value decomposition and principal component analysis, applications to data-driven problems. Intended primarily for students in computer science and other data-focused sciences. Graduate students will be expected to explain how this material relates to their research. Not open to students who have taken Mathematics 216 or 221. Prerequisite: Mathematics 21, 121, 106L, or 111L.