MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
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This course includes
- 28 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 36 lessons • 28 hours of video
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
36 lessons
• 28 hours
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
36 lessons
• 28 hours
- Course Introduction of 18.065 by Professor Strang 07:04
- An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,... 08:07
- Lecture 1: The Column Space of A Contains All Vectors Ax 52:15
- Lecture 2: Multiplying and Factoring Matrices 48:26
- 3. Orthonormal Columns in Q Give Q'Q = I 49:24
- 4. Eigenvalues and Eigenvectors 48:56
- 5. Positive Definite and Semidefinite Matrices 45:27
- 6. Singular Value Decomposition (SVD) 53:34
- 7. Eckart-Young: The Closest Rank k Matrix to A 47:16
- Lecture 8: Norms of Vectors and Matrices 49:21
- 9. Four Ways to Solve Least Squares Problems 49:51
- Lecture 10: Survey of Difficulties with Ax = b 49:36
- Lecture 11: Minimizing ‖x‖ Subject to Ax = b 50:22
- 12. Computing Eigenvalues and Singular Values 49:28
- Lecture 13: Randomized Matrix Multiplication 52:24
- 14. Low Rank Changes in A and Its Inverse 50:34
- 15. Matrices A(t) Depending on t, Derivative = dA/dt 50:52
- 16. Derivatives of Inverse and Singular Values 43:08
- Lecture 17: Rapidly Decreasing Singular Values 50:34
- Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points 49:00
- 19. Saddle Points Continued, Maxmin Principle 52:13
- 20. Definitions and Inequalities 55:01
- Lecture 21: Minimizing a Function Step by Step 53:45
- 22. Gradient Descent: Downhill to a Minimum 52:44
- 23. Accelerating Gradient Descent (Use Momentum) 49:02
- 24. Linear Programming and Two-Person Games 53:34
- 25. Stochastic Gradient Descent 53:03
- 26. Structure of Neural Nets for Deep Learning 53:17
- 27. Backpropagation: Find Partial Derivatives 52:38
- Lecture 30: Completing a Rank-One Matrix, Circulants! 49:53
- 31. Eigenvectors of Circulant Matrices: Fourier Matrix 52:37
- Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule 47:19
- 33. Neural Nets and the Learning Function 56:07
- 34. Distance Matrices, Procrustes Problem 29:17
- 35. Finding Clusters in Graphs 34:49
- Lecture 36: Alan Edelman and Julia Language 38:11
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