Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore geometric probabilistic models in this comprehensive 2.5-hour tutorial from the Uncertainty in Artificial Intelligence conference. Delve into the intersection of geometric deep learning and data-efficient probabilistic modeling techniques, focusing on Gaussian processes and Bayesian neural networks. Learn how these methods enable uncertainty quantification in geometry-compatible ways, crucial for applications in drug design, robotics, and molecular optimization. Discover the theory and practical implementation of these techniques through software demonstrations, and examine emerging applications where data efficiency and geometric properties are paramount. Access accompanying slides to enhance your understanding of this rapidly developing field at the forefront of AI research.