Lecture 23 – Principal Component Analysis
Presented by Raguvir Kunani
Content by Raguvir Kunani, Isaac Schmidt
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
| Video | Quick Check | |
|---|---|---|
| 23.0 Introduction |
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| 23.1 Covariance Matrix |
23.1 | |
| 23.2 Eigenvalues and Eigenvectors |
23.2 | |
| 23.3 Singular Value Decomposition |
23.3 | |
| 23.4 Summarizing data with fewer dimensions |
23.4 | |
| 23.5 Connecting SVD to summarizing data |
23.5 | |
| 23.6 Principal Component Analysis |
23.6 | |
| 23.7 Choosing the number of principal components |
23.7 | |
| 23.8 Interpreting principal components |
23.8 | |
| 23.9 Summary |
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| 23.10 (Bonus) PCA vs. Regression |