Exploring 10 701 Machine Learning Fall 2013 Lecture 20
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- graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight ...
- Introduction to
- Boosting; HMMs and DBNs; overview of MCMC.
- Description.
- Topics: course logistics, high-level overview of
In-Depth Information on 10 701 Machine Learning Fall 2013 Lecture 20
Graphical models: junction trees, belief propagation. Note that the first Introduction to Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians decision trees, bagging, discriminative v. generative.
... rules right both of those will just
That wraps up our extensive overview of 10 701 Machine Learning Fall 2013 Lecture 20.
