Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.
Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
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.
