Optimization and Linear Algebra for Computation

Catalog description

Unified introduction to optimization and linear algebra, and their applications to machine learning. Topics include: vectors, matrices, eigenvalues, least squares, gradient descent, and computational graphs. Emphasis will be given to conceptual and computational aspects with applications to many domains.