Variational Bayesian methods

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Statistical inference
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Related lectures (22)

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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.