Exploring 10 701 Machine Learning Fall 2014 Lecture 9
Exploring 10 701 Machine Learning Fall 2014 Lecture 9 reveals several interesting facts.
- Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity
- Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
- Topics: course logistics, high-level overview of
- Topics: Practice working with probability distributions involving linear algebra and matrix calculus
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM)
In-Depth Information on 10 701 Machine Learning Fall 2014 Lecture 9
Topics: polynomial regression, kernelized regression, Gaussian process (GP) regression Topics: review of d-separation, probably approximately correct (PAC) bounds, Vapnik–Chervonenkis (VC) dimension Topics: linear regression, least squares, polynomial regression Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...
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