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.
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.
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Course Credit: MIT
|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|
|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|
|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|
|Submit Your Assignment||00:00:00|
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