In this video, we talk about what the covariance matrix is and what the values in it represents.
*References*
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Multivariate Normal (Gaussian) Distribution Explained: https://youtu.be/UVvuwv-ne1I
Covariance vs Correlation Explained: https://youtu.be/uW0TapQ6UQU
*Related Videos*
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Marginal, Joint and Conditional Probabilities Explained: https://youtu.be/xu-HhF3SpbE
Why We Don't Use the Mean Squared Error (MSE) Loss in Classification: https://youtu.be/bNwI3IUOKyg
The Bessel's Correction: https://youtu.be/E3_408q1mjo
Gradient Boosting with Regression Trees Explained: https://youtu.be/lOwsMpdjxog
P-Values Explained: https://youtu.be/IZUfbRvsZ9w
Kabsch-Umeyama Algorithm: https://youtu.be/nCs_e6fP7Jo
Eigendecomposition Explained: https://youtu.be/ihUr2LbdYlE
*Contents*
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00:00 - Intro
00:16 - Variance in one dimension
00:53 - Variance in multiple dimensions
01:16 - The main diagonal elements
01:54 - The off diagonal elements
02:35 - Covariance vs correlation
03:03 - Outro
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#covariance #variance #covariancematrix #correlation #statistics