K-Nearest Neighbors & Feature Expansion

This lecture covers the k-Nearest Neighbors method for classification and regression, starting with linear models and progressing to nonlinear machine learning through feature expansion. It explains the concept of polynomial feature expansion and its application in transforming input data for better model fitting. The curse of dimensionality and the properties of k-Nearest Neighbors are discussed, highlighting the importance of choosing the right parameters for effective model performance.