2. Multiplying and Factoring 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
Multiplying and factoring matrices are the topics of this lecture. Professor Strang reviews multiplying columns by rows: AB = sum of rank one matrices. He also introduces the five most important factorizations.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Видео 2. Multiplying and Factoring Matrices канала MIT OpenCourseWare
Instructor: Gilbert Strang
View the complete course: https://ocw.mit.edu/18-065S18
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k
Multiplying and factoring matrices are the topics of this lecture. Professor Strang reviews multiplying columns by rows: AB = sum of rank one matrices. He also introduces the five most important factorizations.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Видео 2. Multiplying and Factoring Matrices канала MIT OpenCourseWare
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![3. Orthonormal Columns in Q Give Q'Q = I](https://i.ytimg.com/vi/Xa2jPbURTjQ/default.jpg)
![1. The Column Space of A Contains All Vectors Ax](https://i.ytimg.com/vi/YiqIkSHSmyc/default.jpg)
![22. Gradient Descent: Downhill to a Minimum](https://i.ytimg.com/vi/AeRwohPuUHQ/default.jpg)
![25. Stochastic Gradient Descent](https://i.ytimg.com/vi/k3AiUhwHQ28/default.jpg)
![Matrix Factorization - Numberphile](https://i.ytimg.com/vi/wTUSz-HSaBg/default.jpg)
![Singular Value Decomposition (the SVD)](https://i.ytimg.com/vi/mBcLRGuAFUk/default.jpg)
![Is BᵀB Always Positive Definite? (Also, Messi makes a comeback!)](https://i.ytimg.com/vi/bp38BKP-xh4/default.jpg)
![Matrices, determinants and the birth of Linear Algebra | Math History | NJ Wildberger](https://i.ytimg.com/vi/Vg1e2FEpf9w/default.jpg)
![A conversation with Gilbert Strang](https://i.ytimg.com/vi/gGYcSjrqbjc/default.jpg)
![10. The Four Fundamental Subspaces](https://i.ytimg.com/vi/nHlE7EgJFds/default.jpg)
![Similar Matrices](https://i.ytimg.com/vi/LKMGo8G7-vk/default.jpg)
![Introduction to projections | Matrix transformations | Linear Algebra | Khan Academy](https://i.ytimg.com/vi/27vT-NWuw0M/default.jpg)
![21. Eigenvalues and Eigenvectors](https://i.ytimg.com/vi/cdZnhQjJu4I/default.jpg)
![Eigenvectors of Symmetric Matrices Are Orthogonal](https://i.ytimg.com/vi/gJhlkEBZsfI/default.jpg)
![36. Alan Edelman and Julia Language](https://i.ytimg.com/vi/rZS2LGiurKY/default.jpg)
![Machine Learning | Singular Value Decomposition](https://i.ytimg.com/vi/4tvw-1HI45s/default.jpg)
![09 Machine Learning: Norms](https://i.ytimg.com/vi/JmxGlrurQp0/default.jpg)
![Bias-Variance Tradeoff : Data Science Basics](https://i.ytimg.com/vi/YIPsfEtJppE/default.jpg)
![17. Orthogonal Matrices and Gram-Schmidt](https://i.ytimg.com/vi/0MtwqhIwdrI/default.jpg)
![7. Solving Ax = 0: Pivot Variables, Special Solutions](https://i.ytimg.com/vi/VqP2tREMvt0/default.jpg)