Explore latent stochastic differential equations for irregularly-sampled time series in this comprehensive seminar on theoretical machine learning. Delve into topics such as ordinary differential equations, latent variable models, ODE latent-variable models, and stochastic transition dynamics. Learn about the Poisson Process Likelihoods, limitations of Latent ODEs, and continuous-time backpropagation. Discover the intricacies of running SDEs backwards, time and memory costs, and variational inference. Gain insights from speaker David Duvenaud of the University of Toronto as he presents advanced concepts in machine learning and data analysis for handling irregularly-timed datasets.