Lecture notes of "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning"
Course: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Mathematics | MIT OpenCourseWareby Gilbert Strang http://t.cn/EKdMLqN
Videos: https://www.bilibili.com/video/av53055190
- Lecture 1: The Column Space of A Contains All Vect
- Lecture 2: Multiplying and Factoring Matrices
- Lecture 3: Orthonormal Columns in Q Give Q^TQ=I
- Lecture 4: Eigenvalues and Eigenvectors
- Lecture 5: Positive Definite and Semidefinite Matrices
- Lecture 6: Singular Value Decomposition (SVD)
- Lecture 7: Eckart-Young: The Closest Rank k Matrices to A
- Lecture 8: Norms of Vectors and Matrices
- Lecture 9: Four Ways to Solve Least Square Problems
- Lecture 10: Survey of Difficulties with Ax=b
- Lecture 11: Minimizing ||x|| Subject to Ax=b
- Lecture 12: Computing Eigenvalues and Singular Values
- Lecture 13: Randomized Matrix Multiplication
- Lecture 14: Low Rank Changes in A and Its Inverse