6.438 Algorithms for Inference
Introduction to statistical inference with probabilistic graphical models. Directed and undirected graphical models, and factor graphs, over discrete and Gaussian distributions; hidden Markov models, linear dynamical systems. Sum-product and junction tree algorithms; forward-backward algorithm, Kalman filtering and smoothing. Min-sum and Viterbi algorithms. Variational methods, mean-field theory, and loopy belief propagation. Particle methods and filtering. Building graphical models from data, including parameter estimation and structure learning; Baum-Welch and Chow-Liu algorithms. Selected special topics.
Lecture occurs 9:30 AM to 11:00 AM on Tuesdays and Thursdays in 4-370.
This class counts for a total of 12 credits.
You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.438&btnG=Google+Search&inurl=https site or on the 6.438 Stellar site.
© Copyright 2015 Yasyf Mohamedali