• Online, Self-Paced
Course Description

This 11-video course explores advanced Bayesian computation models, as well as how to implement Bayesian modeling with linear regression, nonlinear, probabilistic, and mixture models. In addition, learners discover how to implement Bayesian inference models with PyMC3. First, learn how to build and implement Bayesian linear regression models by using Python for machine learning solutions. Examine prominent hierarchical linear models from the perspective of regression coefficients. Then view the concept of probability models and use of Bayesian methods for problems with missing data. You will discover how to build probability models by using Python, and examine coefficient shrinkage with nonlinear models, nonparametric models, and multivariate regression from nonlinear models. Examine fundamental concepts of Gaussian process models; the approaches of classification with mixture models and regression with mixture models; and essential properties of Dirichlet process models. Finally, learn how to implement Bayesian inference models in Python with PyMC3. The concluding exercise recalls hierarchical linear models from the perspective of regression coefficients, and asks learners to describe the approach of working with generalized linear models, and implement Bayesian inference by using PyMC3.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture