Least Squares vs Maximum Likelihood: Understanding the Connection

In this video, we explore why the least squares method is closely related to the Gaussian distribution. Simply put, this happens because it assumes that the errors or residuals in the data follow a normal distribution with a mean on the regression line.

*References*
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Multivariate Normal (Gaussian) Distribution Explained: https://youtu.be/UVvuwv-ne1I

*Related Videos*
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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
00:38 - Linear Regression with Least Squares
01:20 - Gaussian Distribution
02:10 - Maximum Likelihood Demonstration
03:23 - Final Thoughts
04:33 - Outro

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