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This is a graduate-level introduction course 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 and signal processing, artificial intelligence. In addition to that, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: MIT

Course Curriculum

Module 01
Course Overview 00:10:00
Preliminaries 00:10:00
Directed Graphical Models 00:10:00
Undirected Graphical Models 00:10:00
Factor Graphs and Comparing Graphical Model Types 00:10:00
Minimal I-Maps, Chordal Graphs, Trees, and Markov Chains 00:10:00
Gaussian Graphical Models 00:10:00
Inference On Graphs The Elimination Algorithm 00:10:00
Inference On Trees Sum-Product Algorithm 00:10:00
Forward-Backward Algorithm, Sum-Product On Factor Graphs 00:10:00
Sum-Product On Factor Graphs, MAP Elimination 00:10:00
The Max-Product Algorithm 00:10:00
Module 02
Gaussian Belief Propagation 00:10:00
BP on Gaussian Hidden Markov Models Kalman Filtering 00:10:00
The Junction Tree Algorithm 00:10:00
Loopy Belief Propagation and its Properties 00:10:00
Variational Inference 00:10:00
Markov Chain Monte Carlo Methods and Approximate MAP 00:10:00
Approximate Inference Importance Sampling and Particle Filters 00:10:00
Learning Graphical Models 00:10:00
Learning Parameters of an Undirected Graphical Model 00:10:00
Parameter Estimation from Partial Observations 00:10:00
Learning Structure in Directed Graphs 00:10:00
Learning Exponential Family Models 00:10:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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