Implication of Central Limit theorem and its application in machine Learning
Are you familiar with hypothesis testing? Confidence Intervals ? Statistical Significance ? All of these concepts typically rely on the central limit theorem.
One concrete example I can think of relating to ML: Regression
Most approaches to regression typically assume that the error term that appears follows a Gaussian distribution. How do we justify this assumption ? Well the idea is that we can think of the error term as a sum of multiple different error terms.
Now even if these individual error terms do not follow a Gaussian distribution, we can model their sum as a Gaussian because of the Central Limit Theorem.