Contents

Chapters in this book

1. Frontmatter i
2. Contents vii
3. Preface xi
4. About the Author xiii
5. PART I Machine Learning from Multiple Perspectives
6. CHAPTER 1 Overview of Data Science 3
7. CHAPTER 2 Introduction to Machine Learning 17
8. CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics 37
9. CHAPTER 4 “Modern” Machine Learning 55
10. PART II Systems Analytics
11. CHAPTER 5 Systems Theory Foundations of Machine Learning 91
12. CHAPTER 6 State Space Model and Bayes Filter 101
13. CHAPTER 7 The Kalman Filter for Adaptive Machine Learning 113
14. CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation 125
15. CHAPTER 9 Digital Twins 133
16. Epilogue A New Random Field Theory 149
17. Index 155