6.438 Algorithms for Inference

Class Info

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.

This class has 6.008, 6.041B, 6.436, and 18.06 as prerequisites.

6.438 will be offered this semester (Fall 2017). It is instructed by G. Bresler.

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.

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