Learning outcomes

• Understanding the Bayesian methodology, and the foundations of approximate bayesian inference.
• First steps in probabilistic programming in pyro.
• Getting to know the model based machine learning approach:
  • Learn to make the right assumptions.
  • Criticise your own model.
• Getting to know many model types and comparing their performance.
• Applying these models to a wide range of areas.
• Combining probabilistic modelling with deep learning.

Structure of the workshop

Part 1

• Introduction to Bayesian methods and probabilistic programming languages.
• Inference with discrete variables: Who has the right skill set for your job offering?
• Inference with Markov chain Monte Carlo methods: Marriages, divorces and causality.
• The Lotka-Volterra predator-prey model. Advantages and necessity of hybrid approaches combining deterministic and statistical models.

Part 2

• Stochastic variational inference and the mean field assumption.
• Clustering images with mixture models and latent Dirichlet allocation like models.
• Understanding the development of asthma through Markov chains.
• Become a wine connoisseur with Bayesian neural networks.
• Modeling faces through the ages with variational autoencoders.