Program


Note: The schedule will receive some minor updates mainly regarding the talks and poster sessions.

Topics

The summer school will be structured into theoretical lectures and hands-on tutorials. We will have the following modules:

  • Probabilistic models, variational inference and probabilistic programming (Day 1 to Day 3)
    • Introduction to probabilistic modeling
      • Bayesian modeling: prior, likelihood and posterior
      • Concepts of Bayesian networks and latent-variable models
      • Posterior inference and parameter learning
      • Modeling techniques
    • Variational inference
      • Mean-field, CAVI and conjugate models
      • Stochastic Variational Inference and Optimization
      • Black-box variational inference
      • Automatic Differentiation Variational inference
    • Probabilistic programming
      • Introduction to the concept of probabilistic programming
      • Language syntax and semantics
      • Inference mechanisms
  • Deep Generative Models (Day 4 and Day 5)
    • Introduction to Deep Learning
      • Examples of models (ConvNet, RNN, etc.)
      • Learning: stochastic optimization and backpropagation
    • Variational Auto-Encoders
    • Bayesian Neural Networks
    • Combining classical neural networks and probabilistic models