Book Image

Learning Bayesian Models with R

By : Hari Manassery Koduvely
Book Image

Learning Bayesian Models with R

By: Hari Manassery Koduvely

Overview of this book

Table of Contents (16 chapters)
Learning Bayesian Models with R
About the Author
About the Reviewers

An overview of common machine learning tasks

This section is a prequel to the following chapters, where we will discuss different machine learning techniques in detail. At a high level, there are only a handful of tasks that machine learning tries to address. However, for each of such tasks, there are several approaches and algorithms in place.

The typical tasks in any machine learning are one of the following:

  • Classification

  • Regression

  • Clustering

  • Association rules

  • Forecasting

  • Dimensional reduction

  • Density estimation

In classification, the objective is to assign a new data point to one of the predetermined classes. Typically, this is either a supervised or semi-supervised learning problem. The well-known machine learning algorithms used for classification are logistic regression, support vector machines (SVM), decision trees, Naïve Bayes, neural networks, Adaboost, and random forests. Here, Naïve Bayes is a Bayesian inference-based method. Other algorithms, such as logistic regression and neural...