Exploring 10 701 Machine Learning Fall 2014 Lecture 16
Exploring 10 701 Machine Learning Fall 2014 Lecture 16 reveals several interesting facts.
- Topics: graphical models, variable elimination, Bayesian networks, independence relations in graphical models
- Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
- Introduction to
- Topics: principal component analysis (PCA), deep
- Topics: overview of topics that may tested on exam, open Q&A
In-Depth Information on 10 701 Machine Learning Fall 2014 Lecture 16
Topics: d-separation, Bayes ball algorithm, factor graphs, Markov random fields Conjugate Priors Collapsing Entropy / Kraft's inequality Directed graphical models (intro) Introduction to Topics: hidden Markov model (HMM), belief propagation, junction tree algorithm Topics: optimization, gradient descent, Newton's method, convergence analysis
Topics: course logistics, high-level overview of
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