Kernel Ridge Regression: Equivalence, Representer Theorem, and Kernel Trick

This lecture by the instructor covers the topic of Kernel Ridge Regression, focusing on equivalent formulations, the Representer Theorem, and the Kernel Trick. It explains the mathematical expressions for ridge regression, the proof of the Representer Theorem, and the usefulness of alternative forms. The lecture also delves into the concept of kernelized ridge regression, the kernel matrix, embedding into feature spaces, and predicting with kernels. It concludes with discussions on building new kernels from existing ones and Mercer's condition for kernel functions.