MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 — 1d 4h 9m total, 36 videos

1×	1d 4h 9m 9s
1.25×	22h 31m 19s
1.5×	18h 46m 6s
1.75×	16h 5m 14s
2×	14h 4m 35s

Plan to finish

About 29 days at an hour a day

Pick a daily pace and the days you'll watch. Ticked videos are skipped.

At one hour a day, every day, this playlist takes about 29 days. Turn on JavaScript to plan around your week.

Videos (36)

1d 4h 9m in total · tick what you've watched

Tick videos you've watched — progress is saved in this browser.


1	Course Introduction of 18.065 by Professor Strang	7:04	2019-05-16
2	An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,...	8:07	2019-08-19
3	Lecture 1: The Column Space of A Contains All Vectors Ax	52:15	2019-05-16
4	Lecture 2: Multiplying and Factoring Matrices	48:26	2019-05-16
5	3. Orthonormal Columns in Q Give Q'Q = I	49:24	2019-05-16
6	4. Eigenvalues and Eigenvectors	48:56	2019-05-16
7	5. Positive Definite and Semidefinite Matrices	45:27	2019-05-16
8	6. Singular Value Decomposition (SVD)	53:34	2019-05-16
9	7. Eckart-Young: The Closest Rank k Matrix to A	47:16	2019-07-18
10	Lecture 8: Norms of Vectors and Matrices	49:21	2019-05-16
11	9. Four Ways to Solve Least Squares Problems	49:51	2019-05-16
12	Lecture 10: Survey of Difficulties with Ax = b	49:36	2019-05-16
13	Lecture 11: Minimizing ‖x‖ Subject to Ax = b	50:22	2019-05-16
14	12. Computing Eigenvalues and Singular Values	49:28	2019-05-16
15	Lecture 13: Randomized Matrix Multiplication	52:24	2019-05-16
16	14. Low Rank Changes in A and Its Inverse	50:34	2019-05-16
17	15. Matrices A(t) Depending on t, Derivative = dA/dt	50:52	2019-05-16
18	16. Derivatives of Inverse and Singular Values	43:08	2019-05-16
19	Lecture 17: Rapidly Decreasing Singular Values	50:34	2019-05-16
20	Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points	49:00	2019-05-16
21	19. Saddle Points Continued, Maxmin Principle	52:13	2019-05-16
22	20. Definitions and Inequalities	55:01	2019-05-16
23	Lecture 21: Minimizing a Function Step by Step	53:45	2019-05-16
24	22. Gradient Descent: Downhill to a Minimum	52:44	2019-05-16
25	23. Accelerating Gradient Descent (Use Momentum)	49:02	2019-05-16
26	24. Linear Programming and Two-Person Games	53:34	2019-05-16
27	25. Stochastic Gradient Descent	53:03	2019-05-16
28	26. Structure of Neural Nets for Deep Learning	53:17	2019-05-16
29	27. Backpropagation: Find Partial Derivatives	52:38	2019-05-16
30	Lecture 30: Completing a Rank-One Matrix, Circulants!	49:53	2019-05-16
31	31. Eigenvectors of Circulant Matrices: Fourier Matrix	52:37	2019-05-16
32	Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule	47:19	2019-05-16
33	33. Neural Nets and the Learning Function	56:07	2019-05-16
34	34. Distance Matrices, Procrustes Problem	29:17	2019-05-16
35	35. Finding Clusters in Graphs	34:49	2019-05-16
36	Lecture 36: Alan Edelman and Julia Language	38:11	2019-05-16

Embed a badge

Show the watch time in a README or course description — it links back here and updates itself

Markdown

[![Watch time: 1d 4h 9m · 36 videos](https://playlistduration.com/badge/playlist/PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k.svg)](https://playlistduration.com/playlist/PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k)

HTML

<a href="https://playlistduration.com/playlist/PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k"><img src="https://playlistduration.com/badge/playlist/PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k.svg" alt="Watch time: 1d 4h 9m · 36 videos" width="214" height="20"></a>

Add ?speed=1.5 to the image URL for the time at that speed, ?label=Course to change the left text, or ?style=flat-square for square corners.

Want the numbers themselves? GET https://playlistduration.com/api/v1/playlist/PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k returns them as JSON — free, no key. API docs →