Chapter 1: Introduction, overview, and applications
Abstract
1.1 Introduction
1.2 Why is this book important?
1.3 Organization of the book
1.4 Informatics
1.5 Statistics for analytics
1.6 Algorithms for analytics
1.7 Machine learning
1.8 Artificial intelligence
1.9 A platform for building a classifier from the ground up (binary case)
1.10 A platform for building a classifier from the ground up (general case)
1.11 Summary
Chapter 2: Ground truthing
Abstract
2.1 Introduction
2.2 Pre-validation
2.3 Optimizing settings from training data
2.4 Learning how to Learn
2.5 Deep learning to deep unlearning
2.6 Summary
Chapter 3: Experimental design
Chapter 4: Meta-analytic design patterns
Chapter 5: Sensitivity analysis and big system engineering
Chapter 6: Multipatch predictive selection
Chapter 7: Modeling and model fitting
Chapter 8: Synonym-antonym and reinforce-void patterns
Chapter 9: Analytics around analytics
Chapter 10: System design optimization
Chapter 11: Aleatory and expert system techniques
Chapter 12: Application I: Topics and challenges in machine translation, robotics, and biological sciences
Chapter 13: Application II: Medical and health-care informatics, economics, business, and finance
Chapter 14: Discussion, conclusions, and the future of data
Index