Lecture 8: Norms of Vectors and Matrices
49:51
9. Four Ways to Solve Least Squares Problems
53:34
6. Singular Value Decomposition (SVD)
52:15
Lecture 1: The Column Space of A Contains All Vectors Ax
47:16
7. Eckart-Young: The Closest Rank k Matrix to A
48:26
Lecture 2: Multiplying and Factoring Matrices
48:56
4. Eigenvalues and Eigenvectors
49:28
12. Computing Eigenvalues and Singular Values
51:23