Document Analysis and Topic Modeling

This lecture covers document analysis and topic modeling, focusing on discovering recurring themes in text documents and the relationship between documents and topics. It introduces mixture of multinoullis models and Latent Dirichlet Allocation (LDA) as methods to assign topics to documents. The limitations of mixture models are discussed, leading to the introduction of LDA, which allows each word in a document to have its own topic. Variational inference and variational autoencoders are presented as techniques to estimate model parameters and learn meaningful latent representations. The lecture concludes with an overview of deep generative models, including autoencoders, generative adversarial networks (GANs), and conditional GANs.

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