Abstract:
In this presentation, the framework of empirical risk minimization will be introduced. It will turn out that all the commonly used supervised machine learning models such as linear regression, logistic regression, support vector machines and neural networks are special examples under this framework. The excess risk of a regression problem will be computed, followed by deriving the least squares estimators in linear regression.