Lecture 8: Norms of Vectors and Matrices
49:51
9. Four Ways to Solve Least Squares Problems
9:34
The Lp Norm for Vectors and Functions
53:34
6. Singular Value Decomposition (SVD)
16:38
Norms
47:16
7. Eckart-Young: The Closest Rank k Matrix to A
51:23
21. Eigenvalues and Eigenvectors
35:11
Is the Future of Linear Algebra.. Random?
1:05:09