Lecture 2 (Part 2) - Nonlinear Regression (with Kernels, kNN, Features) - Machine Learning Course
Machine Learning course taught by Dr. Mohamed-Rafik Bouguelia, at Halmstad University, Sweden.
After we covered linear regression in the previous part of this machine learning tutorial, in this part of the regression lecture we present nonlinear regression methods. First, the lecture describes how to train or fit a nonlinear regression model based on linear regression + nonlinear features; then, it presents the k-nearest-neighbors (kNN) method for regression (and the weighted kNN). Then it describes kernel regression using a Gaussian kernel function as an example.