He touches on it at the end with Singular Value Decomposition, but didn't mention that you can use SVD to get eigenvectors/values. He also doesn't cover how to

He touches on it at the end with Singular Value Decomposition, but didn't mention that you can use SVD to get eigenvectors/values. He also doesn't cover how to do SVD. This could definitely be covered more in depth, but maybe not in a single semester undergraduate course. I think a follow on set of notes that starts with eigenvectors/values and continues into the more fun parts of linear algebra would be very useful.