This document summarizes a presentation on Bayesian deep learning and probabilistic programming. It discusses: 1. The history of Bayesian neural networks from 1987 to present, focusing on key papers. 2. An overview of Bayesian deep learning methodology, including Bayesian inference, variational inference, and Monte Carlo methods. 3. Probabilistic programming libraries like Edward that combine prob