Ementorhub

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

Markov Model

Unsupervised Learning

R Programming

Mathematical Modeling

Statistical Machine Learning

Statistical Methods

Machine Learning Algorithms

Statistical Modeling

Bayesian Statistics

Probability & Statistics

There are 5 modules in this course

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.

Maximum likelihood estimation for Mixture Models

Bayesian estimation for Mixture Models

Applications of Mixture Models

Practical considerations