Math 416 is a rigorous, abstract treatment of linear algebra. Topics to be covered include vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalizability, and inner product spaces. The course concludes with a brief introduction to the theory of canonical forms for matrices and linear transformations
Just be warned that this is literally the graduate level linear algebra course taken by mathematics majors. If you are looking for applications, this might not be it. On the other hand, if you are looking for a deep understanding of the fundamentals - I would say you found it.
Thank you for posting that info. I'd hate for the guy to want the applied computer-centric linear algebra only to find himself neck deep in a super rigorous course that might go deeper that he intended! Oof.
I should have posted the Math 257 description too. It also has lectures online as well as a synchronous Zoom component:
Introductory course incorporating linear algebra concepts with computational tools, with real world applications to science, engineering and data science. Topics include linear equations, matrix operations, vector spaces, linear transformations, eigenvalues, eigenvectors, inner products and norms, orthogonality, linear regression, equilibrium, linear dynamical systems and the singular value decomposition.
Comments
Just be warned that this is literally the graduate level linear algebra course taken by mathematics majors. If you are looking for applications, this might not be it. On the other hand, if you are looking for a deep understanding of the fundamentals - I would say you found it.
Thank you for posting that info. I'd hate for the guy to want the applied computer-centric linear algebra only to find himself neck deep in a super rigorous course that might go deeper that he intended! Oof.
I should have posted the Math 257 description too. It also has lectures online as well as a synchronous Zoom component:
Introductory course incorporating linear algebra concepts with computational tools, with real world applications to science, engineering and data science. Topics include linear equations, matrix operations, vector spaces, linear transformations, eigenvalues, eigenvectors, inner products and norms, orthogonality, linear regression, equilibrium, linear dynamical systems and the singular value decomposition.