Material Detail

6.438 Algorithms for Inference | Electrical Engineering and Computer Science

This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.

Keywords:: Probability and Statistics, Variational method, Forward-backward algorithm, graphical model, junction tree algorithm, Hidden Markov model, EM algorithm, probabilistic graphical model, statistical inference, factor graph, Sum-product algorithm, inference, Min-sum algorithm, learning structure, markov chain, linear dynamical systems, mean-field theory, Gaussian model, Viterbi algorithm, Algorithms and Data Structures, Kalman filtering, parameter estimation, smoothing, algorithm, loopy belief propagation

Disciplines:

Science and Technology / Engineering / Electrical Engineering

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Material Type:: Online Course
Date Added to MERLOT:: May 12, 2016
Date Modified in MERLOT:: December 2, 2017
Author:: Unknown (Know the author?)
Submitter:: Barbra Bied Sperling

Primary Audience:: Graduate School
Technical Format:: Website
Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States