Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.
Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models
This course is part of Bayesian Statistics Specialization
Instructor: Abel Rodriguez
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4.5
(60 reviews)
Intermediate level
Recommended experience
21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explain the basic principles behind the algorithm for fitting a mixture model.
Compute the expectation and variance of a mixture distribution.
Use mixture models to solve classification and clustering problems, and to provide density estimates.
Skills you'll gain
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Assessments
11 assignments
Taught in English
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This course is part of the Bayesian Statistics Specialization
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There are 5 modules in this course
This module defines mixture models, discusses its properties, and develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning.
What's included
9 videos7 readings7 assignments2 peer reviews1 discussion prompt
What's included
4 videos2 readings2 peer reviews1 discussion prompt
What's included
6 videos2 readings2 peer reviews
What's included
7 videos3 readings3 peer reviews
What's included
7 videos5 readings4 assignments1 peer review1 discussion prompt
Instructor
Instructor ratings
4.6 (29 ratings)Offered by
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4.5
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RL
5
Reviewed on Feb 10, 2023
I really enjoyed this course! Plenty of examples on how to use Mixture Models in a Machine Learning context. Thanks to Abel and his team for putting together such an useful course.
CK
5
Reviewed on Oct 14, 2023
Great course. The professor is excellent. The material is fairly advanced so make sure to take the prerequisites.
SM
5
Reviewed on Jan 19, 2021
I learned a lot about bayesian mixture model, expectation maximization, and MCMC algorithms and their use case in classification and clustering problems. I highly recommend this course.
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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