Lecture 1: Example applications, introduction to probabilistic programming paradigm
Lecture 2: Approaches to inference, different probabilistic programming systems, in-depth introduction to Turing
Lecture 3: Bayesian deep learning, type 2 machine learning systems, GANs & VAEs as generative models
Lecture 4: The Engineering Challenge: Building and deploying probabilistic programming systems.

Tutorial 1: In-depth introduction to Turing
Tutorial 2: Bayesian Deep Learning & Bayesian Differential Equations
Tutorial 3: GANs and VAEs in Probabilistic Programming