Probabilistic ML — Lecture 20 — Latent Dirichlet Allocation
This is the twentieth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the University of Tübingen. Slides available at https://uni-tuebingen.de/en/180804. Contents: How to design probabilistic machine learning solutions Latent Dirichlet Allocation conditional independence (rejoinder) Gibbs sampling (rejoinder) © Philipp Hennig / University of Tübingen, 2021 CC BY-NC-SA 3.0

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Probabilistic ML — Lecture 21 — Efficient Inference and k-Means

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Latent Dirichlet Allocation (Part 1 of 2)

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Probabilistic ML - 20 - Markov Chain Monte Carlo

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An Introduction to Topic Modeling

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Probabilistic ML — Lecture 24 — Variational Inference

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Latent Dirichlet Allocation (LDA) with Gibbs Sampling Explained

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Probabilistic ML - 25 - Revision

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Prof. David Blei - Probabilistic Topic Models and User Behavior

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2 Bouncy Things. Zero bounce.

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Training Latent Dirichlet Allocation: Gibbs Sampling (Part 2 of 2)

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(Original Paper) Latent Dirichlet Allocation (algorithm) | AISC Foundational

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Terence Tao: Nobody Understands Why AI Actually Works

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MIT Just Revealed the AI Bubble's Fatal Flaw

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