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Preface
About the Author
Machine Learning from Multiple Perspectives
Overview of Data Science
Canonical Business Problem
A Basic ML Solution
Systems Analytics
Digital Twins
References
Introduction to Machine Learning
Basic Machine Learning
Normalization
Data Exploration
Parallel Coordinate Systems
Feature Extraction
Multiple Linear Regression
Decision Tree
Naïve Bayes
Ensemble Method
Unsupervised Learning
K-Means Clustering
Self-Organizing Map (SOM) Clustering
Conclusion
Systems Theory, Linear Algebra, and Analytics Basics
Digital Signal Processing (DSP) Machine Learning (ML)
Linear Time Invariant (LTI) System
Linear Algebra
Conclusion
“Modern” Machine Learning
ML Formalism
Bayes
Generalization, the Hoeffding Inequality, and VC Dimension
Formal Learning Methods
Regularization &amp Recursive Least Squares
Revisiting the Iris Problem
Kernel Methods: Nonlinear Regression,
Bayesian Learning, and Kernel Regression
Random Projection Machine Learning
Random Projection Recursive Least Squares (RP-RLS)
ML Ontology
Conditional Expectation and Big Data
Big Data Estimation
Conclusion
Adaptive Machine Learning
What is Dynamics?
References
Systems Analytics
Systems Theory Foundations of
Machine Learning
Introduction-in-Stream Analytics
Basics for Adaptive ML
Exact Recursive Algorithms
State Space Model and Bayes Filter
State-Space Model of Dynamical Systems
Kalman Filter for the State-Space Model
Special Combination of the Bayes Filter and Neural Networks
References
The Kalman Filter for Adaptive Machine Learning
Kernel Projection Kalman Filter
Optimized Operation of the KP-Kalman Filter
Reference
The Need for Dynamical Machine Learning:
The Bayesian Exact Recursive Estimation
Need for Dynamical ML
States for Decision Making
Summary of Kalman Filtering and Dynamical Machine Learning
Digital Twins
Causality
Inverse Digital Twin
Inverse Model Framework
Graph Causal Model
Causality Insights
Inverse Digital Twin Algorithm
Simulation
Conclusion
References
Epilogue
A New Random Field Theory
References
Index