Lectures related to Variational Bayesian methods

Deep Generative Models

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Document Analysis and Topic Modeling

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Topic Models

Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Topic Models: Latent Dirichlet Allocation

Introduces Latent Dirichlet Allocation for topic modeling in documents, discussing its process, applications, and limitations.

Expectation Maximization and Clustering

Covers the Expectation Maximization algorithm and clustering techniques, focusing on Gibbs Sampling and detailed balance.

Personalized Menu Optimization

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

K-means and Gaussian Mixture Model

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Document Analysis: Topic Modeling

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

Text Models: Word Embeddings and Topic Models

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

Variational Inference: Lower Bound and ELBO

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

Gibbs Sampling: Simulated Annealing

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

Gaussian Mixture Models: Data Classification

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

Nonparametric and Bayesian Statistics

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

Introduction to Partial Differential Equations

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

Expectation Maximization: Learning Parameters

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

Derivation of EM for the GMM

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

Stochastic Block Model: Community Detection

Covers the Stochastic Block Model for community detection.