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Linear Regression and Ordinary Least Squared ('OLS') are ancient and yet still useful modeling principles. In this video, I introduce these ideas from the typical machine learning perspective - the loss surface. At the end, I explain how basis expansions push this idea into a flexible and diverse modeling world.

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Sources and Learning More

Over the years, I've learned and re-learned these ideas from many sources, which means there wasn't any primary sources I reference when writing. Nonetheless, I confirmed my definitions with the wikipedia articles [1][2] and chapter 5 of [3] is an informative discussion of basis expansions.

[1] Linear Regression, Wikipedia, [ Ссылка ]

[2] Ordinary Least Squares, Wikipedia, [ Ссылка ]

[3] Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer.

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