Key Techniques: Graphic Models, Deep Generative Models, Variational Inference, Markov Chain Monte Carlo, Bayesian Neural Nets, Gaussian Processes, Bayesian Decision Making, Structure Learning.

Applications: Generative Modeling, Semi-supervised Learning, Crowdsourcing, Anomaly Detection, Unvertainty in Deep Learning, Time-Series Modeling, Game, Reinforcement Learning, Healthcare.