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Last updated:
August 6, 2022
Duration:
Unlimited Duration
FREE
This course includes:
Unlimited Duration
Badge on Completion
Certificate of completion
Unlimited Duration
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 communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.
Course Curriculum
- Course Overview Unlimited
- Preliminaries Unlimited
- Directed Graphical Models Unlimited
- Undirected Graphical Models Unlimited
- Factor Graphs and Comparing Graphical Model Types Unlimited
- Minimal I-Maps, Chordal Graphs, Trees, and Markov Chains Unlimited
- Gaussian Graphical Models Unlimited
- Inference On Graphs: The Elimination Algorithm Unlimited
- Inference On Trees: Sum-Product Algorithm Unlimited
- Forward-Backward Algorithm, Sum-Product On Factor Graphs Unlimited
- Sum-Product On Factor Graphs, MAP Elimination Unlimited
- The Max-Product Algorithm Unlimited
- Gaussian Belief Propagation Unlimited
- BP on Gaussian Hidden Markov Models: Kalman Filtering Unlimited
- The Junction Tree Algorithm Unlimited
- Loopy Belief Propagation and its Properties Unlimited
- Variational Inference Unlimited
- Approximate Inference: Importance Sampling and Particle Filters Unlimited
- Learning Graphical Models Unlimited
- Learning Parameters of an Undirected Graphical Model Unlimited
- Parameter Estimation from Partial Observations Unlimited
- Learning Structure in Directed Graphs Unlimited
- Learning Exponential Family Models Unlimited
About the instructor
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Massachusetts Institute of Technology
