Exploring 10 701 Machine Learning Fall 2013 Lecture 19
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- Graphical models: junction trees, belief propagation. Note that the first
- CMU: 2011 Spring:
- Topics: course logistics, high-level overview of
- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Andrew ...
- Probability; Naive Bayes.
In-Depth Information on 10 701 Machine Learning Fall 2013 Lecture 19
graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight ... Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Lecture Bayesian
Topics: plate notation in graphical models, introduction to
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