daviddoji/18065
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

lecture 8 updates

Browse Source
This commit is contained in:
Steven G. Johnson 2023-02-22 15:36:18 -05:00
parent 4c63b638e3
commit f371ff92ff
1 changed files with 8 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
README.md
View File

@ -130,6 +130,12 @@ The *nice* case of diagonalization is when you have **orthonormal eigenvectors**
**Further reading**: Textbook sections I.9, I.11, III.5. [OCW lecture 7](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-7-eckart-young-the-closest-rank-k-matrix-to-a/) and [lecture 8](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-8-norms-of-vectors-and-matrices/).
## Lecture 8 (Feb 22)
* Mean, variance, and covariance.
* Diagonalizing the covariance matrix: [Principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis).
* [PCA demo](https://nbviewer.org/github/mitmath/1806/blob/fall22/notes/Statistics-and-PCA.ipynb)
* [Least-squares demo](https://nbviewer.org/github/mitmath/1806/blob/fall22/notes/Least-Square%20Fitting.ipynb)
**Further reading**: Textbook sections I.9, V.1, V.4. [OCW lecture 7](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-7-eckart-young-the-closest-rank-k-matrix-to-a/) and [OCW lecture 20](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-20-definitions-and-inequalities/).
## Lecture 9 (Feb 24)
* [Least-squares demo](https://nbviewer.org/github/mitmath/1806/blob/fall22/notes/Least-Square%20Fitting.ipynb)