Variational Bayesian methods

Related lectures (22)

Page 2 of 3

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

Explains variational inference, Jensen's inequality, E-step, M-step, and MCMC sampling.

Covers the concept of Gibbs sampling and its application in simulated annealing.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Covers nonparametric statistics, kernel density estimation, Bayesian principles, and posterior distribution summarization.

Covers the basics of Partial Differential Equations, focusing on heat transfer modeling and numerical solution methods.

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Covers the derivation of the EM algorithm for the Gaussian Mixture Model.

Covers the Stochastic Block Model for community detection.