Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.
Explores Variational Auto-Encoders, Bayesian inference, attention-based latent spaces, and the effectiveness of Transformers in language processing.
Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.
