Program


Note: The schedule is tentative and will receive updates regarding lectures or talks, coffe breaks, lunches, banquet and registration.

Concept

Together with the intentionally small team of invited lecturers, we hope to provide an efficient and quality knowledge transfer through:

  • carefully designed curriculum,
  • tight cooperation between our lecturers,
  • a mix of theoretical lectures and some hands-on tutorials,
  • extra time for participants with our teaching assistants at hand,
  • an innovative lecture room (R2) that allows for a close collaboration between the students and lecturers.

Topics

The summer school will be structured into theoretical lectures with some 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
    • Generative Adversarial Networks
    • Normalizing Flows
    • ODEs and Bayesian Neural Nets