Exploring 10 701 Machine Learning Fall 2013 Lecture 19
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- Topics: course logistics, high-level overview of
- Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
- Probability; Naive Bayes.
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM)
- Introduction to
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 Graphical models: junction trees, belief propagation. Note that the first
Message Passing Dynamic Programming Variational Inequalities and EM (briefly) Introduction to
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