In this chapter, we discussed the concepts behind unsupervised and semi-supervised machine learning, and their Bayesian treatment. We learned two important Bayesian unsupervised models: the Bayesian mixture model and LDA. We discussed in detail the bgmm package for the Bayesian mixture model, and the topicmodels and lda packages for topic modeling. Since the subject of unsupervised learning is vast, we could only cover a few Bayesian methods in this chapter, just to give a flavor of the subject. We have not covered semi-supervised methods using both item labeling and feature labeling. Interested readers should refer to more specialized books in this subject. In the next chapter, we will learn another important class of models, namely neural networks.

#### Learning Bayesian Models with R

##### By :

#### Learning Bayesian Models with R

##### By:

#### Overview of this book

Table of Contents (16 chapters)

Learning Bayesian Models with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Introducing the Probability Theory

The R Environment

Introducing Bayesian Inference

Machine Learning Using Bayesian Inference

Bayesian Regression Models

Bayesian Classification Models

Bayesian Models for Unsupervised Learning

Bayesian Neural Networks

Bayesian Modeling at Big Data Scale

Index

Customer Reviews