Exploring 10 701 Machine Learning Fall 2014 Recitation 9
Let's dive into the details surrounding 10 701 Machine Learning Fall 2014 Recitation 9.
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM) Lecturer: Abu ...
- Topics: support vector
- Topics: Practice working with probability distributions involving linear algebra and matrix calculus Lecturer: Anthony Platanios ...
- Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...
- Topics: overview of topics tested on exam, Q&A Lecturer: Ben Cowley https://piazza.com/cmu/
In-Depth Information on 10 701 Machine Learning Fall 2014 Recitation 9
Topics: review of d-separation, probably approximately correct (PAC) bounds, Vapnik–Chervonenkis (VC) dimension Lecturer: ... Topics: polynomial regression, kernelized regression, Gaussian process (GP) regression Lecturer: Aarti Singh ... Topics: overview of topics that may tested on exam, open Q&A Lecturer: Abu Saparov ... Topics: course logistics, high-level overview of
Topics: review of probability theory, multivariate normal distribution Lecturer: Ben Cowley ...
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Recitation 9.
