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
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Bayesian Statistics: Mixture Models
This course is part of Bayesian Statistics Specialization
Instructor: Abel Rodriguez
10,143 already enrolled
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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
Recommended if you're interested in Data Analysis
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4.5
60 reviews
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Showing 3 of 60
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.
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.
CM
4
Reviewed on May 17, 2021
Definitely quite mathematical in nature. Good way to learn about expectation-maximisation algorithm.
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