Supervised Learning with kNN: Regression Model

This lecture presents a simple mathematical model for supervised learning with k-nearest neighbors (kNN) in the context of regression. The instructor explains the assumptions, the distribution knowledge, and the process to find the best function that minimizes the error. Various concepts such as Bayes, risk minimization, and population are covered.