An introduction to IoT analytics

Table of Contents
1. Introduction

2. Review of Probability Theory

3. Simulation Techniques

4. Hypothesis Testing

5. Multivariable Linear Regression

6. Time Series Forecasting

7. Dimensionality Reduction

8. Clustering Techniques

9. Classification Techniques

10. Artificial Neural Networks

11. Support Vector Machines

12. Hidden Markov Models

This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings. This is an introductory book for readers who have no familiarity with these techniques.

The techniques presented in An Introduction to IoT Analytics come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques.