A thorough introduction to linear algebra, with a focus on applications to data science and statistics. Topics include linear algebra in Euclidean spaces: matrices, vectors, linear independence, determinants, subspaces, bases, change of coordinates, linear transformations, null spaces and ranges, projections, orthogonalization, eigenvalues and eigenvectors; as well as least-squares approximation, spectral decomposition, quadratic forms, convexity, principal component analysis, dimensionality reduction, and approximation in function spaces. Matlab will be used for computation and applications.
Prerequisites: AS.110.107 (Calculus II For Biological and Social Science) or AS.110.109 (Calculus II For Physical Sciences & Engineering) or AS.110.113 (Honors Single Variable Calculus).