Probabilistic ML - 09 - a bit of Gaussian process theory

This is Lecture 9 of the course on Probabilistic Machine Learning in the Summer Term of 2025 at the University of Tübingen, taught by Prof. Philipp Hennig. Contents include eigenfunction analysis of kernels, the construction of reproducing kernel Hilbert spaces, and a probabilistic interpretation of the posterior variance as a worst-case error estimate in the RKHS. Probabilistic ML is an integral part of the curriculum of the International Masters Degree in Machine Learning, alongside associated courses on deep learning, statistical machine learning, reinforcement learning, and much more. Playlist for the course:    • Probabilistic Machine Learning 2025 - Phil...