Course Summary
Learn how to model and analyze data using mixture models in this course. You'll gain skills in clustering, classification, and probability modeling.Key Learning Points
- Discover the applications of mixture models in various fields such as finance, biology, and more
- Learn how to use mixture models for clustering, classification, and probability modeling
- Gain hands-on experience with real-world data and software tools such as R and MATLAB
Related Topics for further study
Learning Outcomes
- Understand the principles of mixture modeling
- Learn how to apply mixture models for clustering, classification, and probability modeling
- Gain practical experience in data analysis with mixture models
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics and probability
- Familiarity with programming languages such as R or MATLAB
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
Similar Courses
- Bayesian Statistics: From Concept to Data Analysis
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Co-founder of Coursera, AI researcher
- Professor of Computer Science, NYU
Related Books
Description
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.
Outline
- Basic concepts on Mixture Models
- Welcome to Bayesian Statistics: Mixture Models
- Installing and using R
- Basic definitions
- Mixtures of Gaussians
- Zero-inflated mixtures
- Hierarchical representations
- Sampling from a mixture model
- The likelihood function
- Parameter identifiability
- An Introduction to R
- Example of a bimodal mixture of Gaussians
- Example of a unimodal and skewed mixture of Gaussians
- Example of a unimodal, symmetric and heavy tailed mixture of Gaussians
- Example of a zero-inflated negative binomial distribution
- Example of a zero-inflated log Gaussian distribution
- Sample code for simulating from a Mixture Model
- Basic definitions
- Mixtures of Gaussians
- Zero-inflated distributions
- Definition of Mixture Models
- Likelihood function for mixture models
- Maximum likelihood estimation for Mixture Models
- EM for general mixtures
- EM for location mixtures of Gaussians
- EM example 1
- EM example 2
- Sample code for EM example 1
- Sample code for EM example 2
- Bayesian estimation for Mixture Models
- Markov Chain Monte Carlo algorithms part 1
- Markov Chain Monte Carlo algorithms, part 2
- MCMC for location mixtures of normals Part 1
- MCMC for location mixtures of normals Part 2
- MCMC Example 1
- MCMC Example 2
- Sample code for MCMC example 1
- Sample code for MCMC example 2
- Applications of Mixture Models
- Density estimation using Mixture Models
- Density Estimation Example
- Mixture Models for Clustering
- Clustering example
- Mixture Models and naive Bayes classifiers
- Linear and quadratic discriminant analysis in the context of Mixture Models
- Classification example
- Sample code for density estimation problems
- Sample EM algorithm for clustering problems
- Sample EM algorithm for classification problems
- Practical considerations
- Numerical stability
- Computational issues associated with multimodality
- Bayesian Information Criteria (BIC)
- Bayesian Information Criteria Example
- Estimating the number of components in Bayesian settings
- Estimating the full partition structure in Bayesian settings
- Example: Bayesian inference for the partition structure
- Sample code to illustrate numerical stability issues
- Sample code to illustrate multimodality issues 1
- Sample code to illustrate multimodality issues 2
- Sample code: Bayesian Information Criteria
- Sample code for estimating the number of components and the partition structure in Bayesian models
- Computational considerations for Mixture Models
- Bayesian Information Criteria (BIC)
- Estimating the partition structure in Bayesian models
Summary of User Reviews
Learn about mixture models with this highly rated course on Coursera. Users found the course to be comprehensive and engaging, with a good balance of theory and practical applications.Key Aspect Users Liked About This Course
The course covers a wide range of topics related to mixture models, including clustering, classification, and density estimation.Pros from User Reviews
- Clear and concise explanations of complex concepts
- Interactive assignments and quizzes that reinforce learning
- Great instructor with extensive knowledge and experience in the field
Cons from User Reviews
- Some users found the pace of the course to be too slow or too fast
- The course assumes a basic understanding of probability and statistics
- Not enough emphasis on real-world applications of mixture models
Abel Rodriguez
4.7 Raiting
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