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Frontmatter

Contents

Preface

About the Author

CHAPTER 1 Overview of Data Science

CHAPTER 2 Introduction to Machine Learning

CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics

CHAPTER 4 “Modern” Machine Learning

CHAPTER 5 Systems Theory Foundations of Machine Learning

CHAPTER 6 State Space Model and Bayes Filter

CHAPTER 7 The Kalman Filter for Adaptive Machine Learning

CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation

CHAPTER 9 Digital Twins

Epilogue A New Random Field Theory

Index

Companion Files.zip

Madhavan, P. G.

This book introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code that creates a common framework and points out various interconnections related to industry. This will allow the reader to connect previous subject knowledge to data science, machine learning, or analytics and apply it to IoT applications. Part One brings together subjects in machine learning, systems theory, linear algebra, digital signal processing, and probability theory.

This book introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code...

Format

Data Science for IoT Engineers

A Systems Analytics Approach

P. G. Madhavan

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