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. Prospective math majors should take Mathematics 221 instead. Not open to students who have taken Mathematics 216 or 221. Recommended prerequisite: Mathematics 21, 121, 106L, or 111L.
Not open to students who have taken Mathematics 216 or 221