Introduction to 10 701 Machine Learning Fall 2014 Lecture 20
If you are looking for information about 10 701 Machine Learning Fall 2014 Lecture 20, you have come to the right place. Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
10 701 Machine Learning Fall 2014 Lecture 20 Comprehensive Overview
Topics: course logistics, high-level overview of Topics: principal component analysis (PCA), deep Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...
Introduction to
Summary & Highlights for 10 701 Machine Learning Fall 2014 Lecture 20
- Graphical models: junction trees, belief propagation. Note that the first
- Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity
- Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
- Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
- Topics: linear regression, least squares, polynomial regression
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