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* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Multivariate Normal (Gaussian) Distribution Explained: https://youtu.be/UVvuwv-ne1I *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 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* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 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 *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic https://twitter.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #svd #singularvaluedecomposition #eigenvectors #eigenvalues #linearalgebra