6.438 | Fall 2014 | Graduate

Algorithms for Inference

Course Description

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 …
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.
Learning Resource Types
Lecture Notes
Problem Sets
Exams
Rendering of two robots playing a game.
The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. (Image courtesy of Nebraska Oddfish on Flickr. CC BY-NC-SA 2.0.)