Exploring 10 701 Machine Learning Fall 2013 Lecture 19
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- Probability; Naive Bayes.
- Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
- Topics: course logistics, high-level overview of
- Topics: plate notation in graphical models, introduction to
- Topics: classification, naive Bayes, introduction to maximum likelihood estimation (MLE), and maximum a posteriori estimation ...
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 Graphical models: junction trees, belief propagation. Note that the first Lecture
10-701 Machine Learning Recitation 10: Graphical Models
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