Quick Summary: 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
10 701 Machine Learning Fall 2014 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 Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
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- 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
- Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
- Topics: polynomial regression, kernelized regression, Gaussian process (GP) regression
- Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
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