Probabilistic Generative Models: Schedule
Lecture 1
Outline:
Course outline: typology of models (locally normalized models, globally normalized models, implicit models), methods (stochastic approximation, variational methods, ….)
Probability remdinders
MonteCarlo 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: MetropolisHastings and Gibbs sampling
MCMC application: Sigmoid belief networks and Boltzmann Machines
MCMC exercise: download notebook or view as html
Lecture 4
Lecture 5
Change of variable theorem
Normalizing flows
End of lecture on the logdet term for real NVP flows: page 1 page 2 page 3
Normalzing flows examples:
