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In this course, we focus advanced methods to represent, sample, update and propagate uncertainty. The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in Science and Engineering. This is a “hands on” course, methodology will be coupled with applications. It will include lectures, invited talks, reviews and projects and will meet to discuss a method and its applications.


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 need 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

Course Curriculum

Quantifying Uncertainty Introduction 00:27:00
In A Linear World 00:26:00
Ensemble Filter Smoother 00:28:00
Exponential Family 00:24:00
Expectation Maximization and Model Selection 00:30:00
Markov Chain Monte Carlo 00:26:00
Hierarchical Bayesian Models 00:27:00
Particle Filter 00:25:00
Graphical Models 00:32:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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