Linear regression is a commonly used technique, and parts of it are often justified by heuristics. In this video, we discuss the assumptions behind linear regression, and we show how it can be derived from statistical principles. We focus on the use of mean squared error as the error metric and regularization terms, showing how they are both derived from assumptions about the nature of noise in our data.

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