Media Summary: Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM) Lecturer: Abu ... Topics: linear regression, least squares, polynomial regression Lecturer: Aarti Singh ... Topics: Practice working with probability distributions involving linear algebra and matrix calculus Lecturer: Anthony Platanios ...
10 701 Machine Learning Fall 2014 Recitation 8 - Detailed Analysis & Overview
Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM) Lecturer: Abu ... Topics: linear regression, least squares, polynomial regression Lecturer: Aarti Singh ... Topics: Practice working with probability distributions involving linear algebra and matrix calculus Lecturer: Anthony Platanios ... Topics: overview of topics that may tested on exam, open Q&A Lecturer: Abu Saparov ... Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ... Topics: review of d-separation, probably approximately correct (PAC) bounds, Vapnik–Chervonenkis (VC) dimension Lecturer: ...
Topics: introduction to optimization and convexity, gradient descent, backtracking line search Lecturer: Anthony Platanios ... Topics: course logistics, high-level overview of Topics: review of probability theory, multivariate normal distribution Lecturer: Ben Cowley ... 10-701 Machine Learning Recitation 10: Graphical Models
