Lecture 1: The Column Space of A Contains All Vectors Ax
52:14Lecture 2: Multiplying and Factoring Matrices
48:253. Orthonormal Columns in Q Give Q'Q = I
49:244. Eigenvalues and Eigenvectors
48:555. Positive Definite and Semidefinite Matrices
45:276. Singular Value Decomposition (SVD)
53:337. Eckart-Young: The Closest Rank k Matrix to A
47:16Lecture 8: Norms of Vectors and Matrices (18.065)
49:21Lecture 9: Four Ways to Solve Least Squares Problems (18.065)
49:51Lecture 10: Survey of Difficulties with Ax = b (18.065)
49:36Lecture 11: Minimizing "x" Subject to Ax = b (18.065)
50:22Lecture 12: Computing Eigenvalues and Singular Values (18.065)
49:28Lecture 13: Randomized Matrix Multiplication (18.065)
52:24Lecture 14: Low Rank Changes in A and Its Inverse (18.065)
50:34Lecture 15: Matrices A(t) Depending on t, Derivative = dA/dt (18.065)
50:52Lecture 16: Derivatives of Inverse and Singular Values (18.065)
43:08Lecture 17: Rapidly Decreasing Singular Values (18.065)
50:34Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points (18.065)
49:00Lecture 19: Saddle Points Continued, Maxmin Principle (18.065)
52:13Lecture 20: Definitions and Inequalities (18.065)
55:015. Positive Definite and Semidefinite Matrices
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
4. Eigenvalues and Eigenvectors
Next · Lecture 66. Singular Value Decomposition (SVD)