Introduction to 10 701 Machine Learning Fall 2013 Lecture 18
Exploring 10 701 Machine Learning Fall 2013 Lecture 18 reveals several interesting facts. Lecture 18
10 701 Machine Learning Fall 2013 Lecture 18 Comprehensive Overview
Topics: plate notation in graphical models, introduction to graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight ... Message Passing Dynamic Programming Variational Inequalities and EM (briefly) Introduction to
Probability; Naive Bayes.
Summary & Highlights for 10 701 Machine Learning Fall 2013 Lecture 18
- Graphical models: junction trees, belief propagation. Note that the first
- Topics: bag of words, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- 10
- Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
- Introduction to
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