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January 28, 2025
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This course is part of Bayesian Statistics Specialization
Instructor: Abel Rodriguez
10,191 already enrolled
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(60 reviews)
Recommended experience
Intermediate level
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
(60 reviews)
Recommended experience
Intermediate level
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
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.
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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.
Some exercises require the use of R, a freely-available statistical software package. A brief tutorial is provided, but we encourage you to take advantage of the many other resources online for learning R if you are interested. This is an intermediate-level course, and it was designed to be the third in UC Santa Cruz's series on Bayesian statistics, after Herbie Lee's "Bayesian Statistics: From Concept to Data Analysis" and Matthew Heiner's "Bayesian Statistics: Techniques and Models." To succeed in the course, you should have some knowledge of and comfort with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
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.
9 videos7 readings7 assignments2 peer reviews1 discussion prompt
4 videos2 readings2 peer reviews1 discussion prompt
6 videos2 readings2 peer reviews
7 videos3 readings3 peer reviews
7 videos5 readings4 assignments1 peer review1 discussion prompt
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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.
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.
Reviewed on May 17, 2021
Definitely quite mathematical in nature. Good way to learn about expectation-maximisation algorithm.
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