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
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
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:
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