I'd rather not bash my undergrad, but suffice to say I tutored intro linear algebra and was very comfortable with eigenvalues, eigenvectors, Gaussian eliminatio

I'd rather not bash my undergrad, but suffice to say I tutored intro linear algebra and was very comfortable with eigenvalues, eigenvectors, Gaussian elimination, and that kind of stuff. What was tricky in 224d was taking the gradients with respect to specific components of a matrix. In the end you get comfortable with what the result should look like, but if you actually write the matrix indices down, it's quite hairy (mostly tensor product(s) that can be rewritten as matrix outer products).