| S# |
Lecture |
Course |
Institute |
Instructor |
Discipline |
| 1501 |
Lecture 1: The Column Space of A Contains All Vectors Ax (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1502 |
Lecture 20: Definitions and Inequalities (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1503 |
Lecture 35: Finding Clusters in Graphs (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1504 |
Lecture 21: Minimizing a Function Step by Step (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1505 |
Lecture 36: Alan Edelman and Julia Language (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Alan Edelman
|
Basic and Health Sciences
|
| 1506 |
Lecture 22: Gradient Descent: Downhill to a Minimum (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1507 |
Lecture 3: Orthonormal Columns in Q Give Q’Q = I (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
53
|
Basic and Health Sciences
|
| 1508 |
Lecture 23: Accelerating Gradient Descent (Use Momentum) (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1509 |
Lecture 4: Eigenvalues and Eigenvectors (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1510 |
Lecture 24: Linear Programming and Two-Person Games (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1511 |
Lecture 5: Positive Definite and Semidefinite Matrices (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1512 |
Lecture 25: Stochastic Gradient Descent (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1513 |
Lecture 6: Singular Value Decomposition (SVD) (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1514 |
Lecture 26: Structure of Neural Nets for Deep Learning (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1515 |
Lecture 7: Eckart-Young: The Closest Rank k Matrix to A (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1516 |
Lecture 27: Backpropagation: Find Partial Derivatives (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1517 |
Lecture 8: Norms of Vectors and Matrices (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1518 |
Lecture 2: Multiplying and Factoring Matrices (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1519 |
Lecture 9: Four Ways to Solve Least Squares Problems (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1520 |
Lecture 30: Completing a Rank-One Matrix, Circulants! (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1521 |
Lecture 31: Eigenvectors of Circulant Matrices: Fourier Matrix (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1522 |
Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1523 |
Lecture 33: Neural Nets and the Learning Function (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1524 |
Lecture 34: Distance Matrices, Procrustes Problem (M-I-T)
|
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)
|
MIT
|
Prof. Dr. Gilbert Strang
|
Basic and Health Sciences
|
| 1525 |
Session 10 Project 3 Check-In; UI and Usability (MIT)
|
Creating Video Games (M-I-T)
|
MIT
|
Philip B. Tan, Richard Eberhardt, Sara Verrilli, Andrew Grant
|
Applied Sciences
|
