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Contents
Published Online on: Dec 31, 2021
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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