Courses and educational materials on Geometric Deep Learning and AI foundations.
A first course on probabilistic machine learning: Bayesian inference, linear regression, classification, neural networks, kernel methods, Gaussian processes, and ensemble models. 64 lectures with videos and slides.
A comprehensive course on the mathematical foundations of geometric deep learning. Topics include group theory, representation theory, equivariant convolutions, and their applications in computer vision and medical imaging.