Gaussian Process Regression: Kernels and Comparisons

This lecture covers Gaussian Process Regression (GPR) focusing on kernel functions, including linear and RBF kernels, and their impact on computational cost and model performance. It also compares GPR with Ridge Regression, highlighting similarities in the nonlinear regressor expressions. The instructor discusses the effect of kernel width and polynomial order on GPR results, as well as the use of nonstationary kernels to capture local variations in data density. The lecture concludes with a comparison of non-linear regression techniques such as SVR, RVR, and GPR, emphasizing differences in algorithm complexity, hyperparameter estimation, and computational costs.