Data Science for IoT Engineers

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Part One
1: Machine Learning from Multiple Perspectives
2: Introduction to Machine Learning
3: Systems Theory, Linear Algebra, and Analytics Basics
4: Modern Machine Learning
Part Two: Systems Analytics
5: Systems Theory Foundations of Machine Learning
6: State Space Model and Bayes Filter
7: The Kalman Filter for Adaptive Machine Learning
8: The Need for Dynamical Machine Learning
9: Digital Twins
Epilogue: A New Random Field Theory
Index