Nonlinear Regression: Gaussian Mixture Regression

This lecture covers Gaussian mixture regression, starting with learning the joint distribution and computing the regressive signal. It explores the uniqueness of the solution, the accuracy of fit with multiple Gauss functions, and the potential for overfitting. The instructor demonstrates how GMR handles noise, interpolation, and the impact of initialization on the solution. The lecture concludes by comparing results and discussing the uniqueness of solutions in regression.