Eckart-Young: The Closest Rank k Matrix to A
47:16Norms of Vectors and Matrices
49:21Four Ways to Solve Least Squares Problems
49:51Survey of Difficulties with Ax = b
49:36Minimizing "x" Subject to Ax = b
50:22Computing Eigenvalues and Singular Values
49:28Randomized Matrix Multiplication
52:24Low Rank Changes in A and Its Inverse
50:34Matrices A(t) Depending on t, Derivative = dA/dt
50:52Derivatives of Inverse and Singular Values
43:08Rapidly Decreasing Singular Values
50:34Counting Parameters in SVD, LU, QR, Saddle Points
49:00Saddle Points Continued, Maxmin Principle
52:13Definitions and Inequalities
55:01Four Ways to Solve Least Squares Problems
Professor Strang explores four fundamental methods for solving least squares problems, essential for data fitting and numerous practical applications.
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Instructor: Gilbert Strang
View the complete course: https://ocw.mit.edu/18-065S18
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k