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Table of Contents
Acknowledgements; Author's Note; Contents; About the Author; Building Foundation: Decoding Knowledge Acquisition; 1 Introduction: Patterns Apart; 1.1 A Naked World of Data Warriors!; 1.2 Introduction-The Blind Data Game; 1.3 Putting Creativity on Weak Legs: Can We Make Present Machines Creative?; 1.4 Learning Using Creative Models; 1.5 Plundered Every Data Point-Data Rich Knowledge Poor Society; 1.6 Computational Creativity and Data Analysis; 1.7 Simple Paradigms and Evaluations: (Machine Learning Compass and Barometer)
1.8 After All Its Time for Knowledge Innovation-Do not just Build Innovate1.9 What Is Knowledge Innovation? (Meta-Knowledge Approach); 1.10 Knowledge Innovation Model Building; 1.11 Creative Intelligence to Collective Knowledge Innovation: (Intelligible Togetherness); 1.12 Do not Dive Deep Unnecessarily: (Your Machine Learning Life Guard in Deep Data Sea); 1.13 Machine Learning and Knowledge Innovation; 1.14 Making Intelligent Agent Intelligent; 1.15 Architecting Intelligence; 1.16 Summary; 2 Understanding Machine Learning Opportunities
2.1 Understanding Learning Opportunity (Catching Data Signals Right)2.2 Knowledge Innovation Building Blocks of ML and Intelligent Systems; 2.3 Stages in Limited Exploration; 2.4 Mathematical Equations for Classification; 2.5 New Paradigms in This Book; 2.6 iknowlation's IDEA Matrix for Machine Learning Opportunity Evaluation; 2.7 Using IDEA Matrix to Identify ML Opportunity; 2.8 Self-evaluation of Learning; 2.9 Mathematical Model of Learnability; 2.10 Building Machine Learning Models: Your Foundation for Surprising Solutions; 2.11 Opportunity Cycle; 2.12 ML Big Landscape
2.13 Context-Based Learning-Respect Heterogeneity2.14 Summary; 3 Systemic Machine Learning; 3.1 What Is a System? (Decoding Connectivity); 3.2 What Is Systemic Machine Learning: (Exploiting Togetherness); 3.3 Systemic Machine Learning Model and Algorithm Selection; 3.4 Cognitive Systemic Machine Learning Models; 3.5 Cognitive Interaction Centric Models; 3.6 Meta-Reasoning Centric Models (System of System); 3.6.1 System Study; 3.6.2 Learning with Limited Data; 3.7 Summary; 4 Reinforcement and Deep Reinforcement Machine Learning; 4.1 Introduction; 4.2 Reinforcement Learning; 4.3 Learning Agents
4.4 Returns and Reward Calculations (Evaluate Your Position and Actions)4.5 Dynamic Systems (Making Best Use of Unpredictability); 4.6 Dynamic Environment and Dynamic System; 4.7 Reinforcement Learning and Exploration; 4.8 Markov Property and Markov Decision Process; 4.9 Value Functions; 4.10 Action and Value; 4.11 Learning an Optimal Policy (Model-Based and Model-Free Methods); 4.12 Uncertainty; 4.13 Adaptive Dynamic Learning (Learning Evolution); 4.14 Temporal Difference (TD) Learning; 4.15 Q Learning; 4.16 Unified View; 4.17 Deep Exploratory Machine Learning; 4.18 Summary
1.8 After All Its Time for Knowledge Innovation-Do not just Build Innovate1.9 What Is Knowledge Innovation? (Meta-Knowledge Approach); 1.10 Knowledge Innovation Model Building; 1.11 Creative Intelligence to Collective Knowledge Innovation: (Intelligible Togetherness); 1.12 Do not Dive Deep Unnecessarily: (Your Machine Learning Life Guard in Deep Data Sea); 1.13 Machine Learning and Knowledge Innovation; 1.14 Making Intelligent Agent Intelligent; 1.15 Architecting Intelligence; 1.16 Summary; 2 Understanding Machine Learning Opportunities
2.1 Understanding Learning Opportunity (Catching Data Signals Right)2.2 Knowledge Innovation Building Blocks of ML and Intelligent Systems; 2.3 Stages in Limited Exploration; 2.4 Mathematical Equations for Classification; 2.5 New Paradigms in This Book; 2.6 iknowlation's IDEA Matrix for Machine Learning Opportunity Evaluation; 2.7 Using IDEA Matrix to Identify ML Opportunity; 2.8 Self-evaluation of Learning; 2.9 Mathematical Model of Learnability; 2.10 Building Machine Learning Models: Your Foundation for Surprising Solutions; 2.11 Opportunity Cycle; 2.12 ML Big Landscape
2.13 Context-Based Learning-Respect Heterogeneity2.14 Summary; 3 Systemic Machine Learning; 3.1 What Is a System? (Decoding Connectivity); 3.2 What Is Systemic Machine Learning: (Exploiting Togetherness); 3.3 Systemic Machine Learning Model and Algorithm Selection; 3.4 Cognitive Systemic Machine Learning Models; 3.5 Cognitive Interaction Centric Models; 3.6 Meta-Reasoning Centric Models (System of System); 3.6.1 System Study; 3.6.2 Learning with Limited Data; 3.7 Summary; 4 Reinforcement and Deep Reinforcement Machine Learning; 4.1 Introduction; 4.2 Reinforcement Learning; 4.3 Learning Agents
4.4 Returns and Reward Calculations (Evaluate Your Position and Actions)4.5 Dynamic Systems (Making Best Use of Unpredictability); 4.6 Dynamic Environment and Dynamic System; 4.7 Reinforcement Learning and Exploration; 4.8 Markov Property and Markov Decision Process; 4.9 Value Functions; 4.10 Action and Value; 4.11 Learning an Optimal Policy (Model-Based and Model-Free Methods); 4.12 Uncertainty; 4.13 Adaptive Dynamic Learning (Learning Evolution); 4.14 Temporal Difference (TD) Learning; 4.15 Q Learning; 4.16 Unified View; 4.17 Deep Exploratory Machine Learning; 4.18 Summary