Matrices and Vector Spaces

3 credits · Graduate

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.