Develop a foundation of basic linear algebra concepts useful in modern signal processing, including vector spaces, norms and inner products, orthogonality, linear operators, matrix inverses, approximations in vector spaces, eigenvalues, and eigenvectors.

Understand advanced topics in linear algebra and some of their applications to real-world engineering problems, including important matrix factorization techniques, singular value decomposition, and special matrices and their applications.

Develop a basic familiarity with the theory of constrained optimization.