Back to Bayesian Statistics: Mixture Models
Learner Reviews & Feedback for Bayesian Statistics: Mixture Models by University of California, Santa Cruz
58 ratings
About the Course
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.
Top reviews
SM
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.
RL
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.
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