Probabilistic Generative Models: Schedule

Lecture 1


  • Course outline: typology of models (locally normalized models, globally normalized models, implicit models), methods (stochastic approximation, variational methods, ….)

  • Probability remdinders

  • Monte-Carlo estimation and importance sampling

  • Sigmoid belief networks (SBN)

  • Gaussian mixture models (GMM)

Code examples:

Lecture 2

For more details about convex analysis and optimization, the the Convex Optimization book by Stephen Boyd and Lieven Vandenberghk is available online.

Lecture 3

  • Globally normalized models and Boltzmann machines

  • Variational methods application: upper and lower bounds for restricted Boltzmann machines

  • MCMC Sampling: Metropolis-Hastings and Gibbs sampling

  • MCMC application: Sigmoid belief networks and Boltzmann Machines

  • MCMC exercise: download notebook or view as html

Lecture 4

  • Variational Auto-Encoders

  • Reparameterization trick

  • Score function estimator

  • Differentiable approximate sampling

Lecture 5