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Intro; Dedication; Foreword; Preface; Acknowledgments; Contents; Contributors; About the Editors; Part I: Introduction; Chapter 1: Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective; 1.1 Introduction; 1.2 Some Terminology; 1.3 A Few Paragraphs on the History of Machine Learning; 1.4 Machine Learning in Ecology and Wildife Biology to Date; 1.5 Algorithms as a Bottleneck for Wildlife Conservation; 1.6 Data Issues and Availability Related to Data Mining and Machine Learning.
1.7 Workflows1.8 Citizen Science; 1.9 A Great Future could be around the Corner, Waiting for you Online, and in the Wilderness of this World; References; Chapter 2: Use of Machine Learning (ML) for Predicting and Analyzing Ecological and 'Presence Only' Data: An Overview of Applications and a Good Outlook; 2.1 Introduction; 2.2 Popular and Widely Available Machine Learning Techniques; 2.3 Applications of Machine Learning in Wildlife Biology; 2.4 Strengths and Some Described Weaknesses of Machine Learning; 2.5 A Case Example.
2.6 Machine Learning in Climate Change Models and Other Complex Applications2.7 Conclusions: Future Outlook and Topics Awaiting Research and Application for Machine Learning (ML); References; Chapter 3: Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees; 3.1 Introduction; 3.2 A Quick Refresher on Linear Models (LMs), Parsimony and Classification and Regression Trees (CARTs); 3.3 Boosting; 3.3.1 What Boosting is in a Nutshell.
3.3.2 Short History of 'Boosting'3.3.3 Why is Boosting so Powerful?; 3.4 Bagging; 3.4.1 What Bagging is in a Nutshell; 3.4.2 Short History of Bagging; 3.4.3 Why Bagging is so Powerful; 3.5 Ensemble Models; 3.5.1 What is an Ensemble Model?; 3.5.2 History of Ensemble Models; 3.5.3 Why Ensemble Models are so Powerful; 3.6 Model Applications and Inference; 3.6.1 Boosting Experiences and Applications; 3.6.2 Bagging Experiences and Applications; 3.6.3 Ensembles; 3.6.4 Precautionary, Pro-Active, and Predictive Models for Better Resource Conservation Management.
3.7 A Commonly Heard Criticism and Misunderstanding of Machine Learning, and Characteristics of Man-Made Science and Conservation Driven by Reductionism3.8 Synthesis and Outlook; References; Part II: Predicting Patterns; Chapter 4: From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared Experiences, Intellectual Reasoning and Analysis Steps for the Real World of Science Applications; 4.1 Introduction; 4.2 Model Selection with Many Predictors as an Analysis Scheme and as a Major Platform for Statistical Testing, Prediction and Inference.
1.7 Workflows1.8 Citizen Science; 1.9 A Great Future could be around the Corner, Waiting for you Online, and in the Wilderness of this World; References; Chapter 2: Use of Machine Learning (ML) for Predicting and Analyzing Ecological and 'Presence Only' Data: An Overview of Applications and a Good Outlook; 2.1 Introduction; 2.2 Popular and Widely Available Machine Learning Techniques; 2.3 Applications of Machine Learning in Wildlife Biology; 2.4 Strengths and Some Described Weaknesses of Machine Learning; 2.5 A Case Example.
2.6 Machine Learning in Climate Change Models and Other Complex Applications2.7 Conclusions: Future Outlook and Topics Awaiting Research and Application for Machine Learning (ML); References; Chapter 3: Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees; 3.1 Introduction; 3.2 A Quick Refresher on Linear Models (LMs), Parsimony and Classification and Regression Trees (CARTs); 3.3 Boosting; 3.3.1 What Boosting is in a Nutshell.
3.3.2 Short History of 'Boosting'3.3.3 Why is Boosting so Powerful?; 3.4 Bagging; 3.4.1 What Bagging is in a Nutshell; 3.4.2 Short History of Bagging; 3.4.3 Why Bagging is so Powerful; 3.5 Ensemble Models; 3.5.1 What is an Ensemble Model?; 3.5.2 History of Ensemble Models; 3.5.3 Why Ensemble Models are so Powerful; 3.6 Model Applications and Inference; 3.6.1 Boosting Experiences and Applications; 3.6.2 Bagging Experiences and Applications; 3.6.3 Ensembles; 3.6.4 Precautionary, Pro-Active, and Predictive Models for Better Resource Conservation Management.
3.7 A Commonly Heard Criticism and Misunderstanding of Machine Learning, and Characteristics of Man-Made Science and Conservation Driven by Reductionism3.8 Synthesis and Outlook; References; Part II: Predicting Patterns; Chapter 4: From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared Experiences, Intellectual Reasoning and Analysis Steps for the Real World of Science Applications; 4.1 Introduction; 4.2 Model Selection with Many Predictors as an Analysis Scheme and as a Major Platform for Statistical Testing, Prediction and Inference.