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This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. .

This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in 'big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice

Introduction -- Secure Cooperative Learning in Early Years -- Outsourced Computation for Learning -- Secure Distributed Learning -- Learning with Differential Privacy -- Applications - Privacy-Preserving Image Processing -- Threats in Open Environment -- Conclusion.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Intro -- Preface -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Introduction to Computer Vision and Machine Learning Applications in Agriculture -- 1 Introduction -- 2 Computer Vision and Machine Learning in Agriculture -- 3 Challenges and Future Scopes -- 4 Conclusion -- References -- Robots and Drones in Agriculture-A Survey -- 1 Introduction -- 2 Robotics Basic -- 2.1 Robotic Mechanism -- 2.2 Agricultural Robot Classification -- 3 Robots in Agricultural Applications -- 3.1 Robots in Path Navigation -- 3.2 Robots in Crop Production -- 3.3 Robots in Weed Removal and Disease and Pest Control -- 3.4 Robots in Crop Harvesting -- 4 Drones in Agriculture -- 5 Commercialization and Current Challenges of Agricultural Robots -- 6 Conclusion -- References -- Detection of Rotten Fruits and Vegetables Using Deep Learning -- 1 Introduction -- 2 Computer Vision and Machine Learning in Fruits and Vegetable Processing -- 2.1 Segmentation and Detection of Fruits and Vegetables from the Natural Environment -- 2.2 Classification of Fruits and Vegetables -- 2.3 Grading of Fruits and Vegetables -- 2.4 Sorting the Defective Fruits and Vegetables -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Convolutional Neural Network -- 3.3 Proposed Convolutional Neural Network Architecture -- 3.4 AlexNet Architecture -- 4 Experimentation and Results -- 5 Discussion -- 6 Conclusion -- References -- Deep Learning-Based Essential Paddy Pests' Filtration Technique for Economic Damage Management -- 1 Introduction -- 2 Related Works -- 3 Pests Classification -- 3.1 Beneficial Pests -- 3.2 Non-beneficial Pests -- 4 Methodology -- 5 Deep Learning -- 5.1 Convolutional Neural Network -- 6 Experiments -- 6.1 Dataset -- 6.2 Experimental Setup -- 6.3 Confusion Matrix -- 6.4 Computation Time -- 7 Conclusion -- References.

User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the residents activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers of the related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living. Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction.

Intro -- Zum Inhalt/Zu den Autoren -- Titel -- Vorwort -- Bearbeiterverzeichnis -- Im Einzelnen haben bearbeitet -- Inhaltsverzeichnis -- Kapitel 1 Einführung -- I. Definitionsversuche -- II. Zur Geschichte der KI -- III. Gang der Darstellung -- 1. KI-Grundlagen und Verständnisbildung zwischen Informatikern und Juristen -- 2. Europarechtliche Perspektive -- 3. Deliktische Haftung und Produkthaftung -- 4. KI-Vertragsrecht -- 5. Rechtsdogmatik -- 6. Geistiges Eigentum -- 7. Datenschutz -- 8. Gesellschaftsrecht -- 9. Weitere Rechtsgebiete: Verbraucherschutz, Arbeitsrecht, ­Strafrecht, anwaltliches Berufsrecht -- 10. Aufsichtsrecht -- IV. Exkurs: Rechtsanwendung durch KI? -- V. Ausblick -- Kapitel 2 Technische Hintergründe -- Kapitel 2.1 Technische Grundlagen -- I. Einleitung -- II. Die vier technischen Bereiche der "Künstlichen Intelligenz" -- 1. Mustererkennung -- a) Explizit programmierte, analytische Verfahren -- b) Selbstlernende Verfahren der Mustererkennung -- 2. Maschinelles Lernen -- a) Automatische Generierung von Kategorisierungen -- b) Automatische Ermittlung von Zusammenhängen -- c) Neuronale Netze und Deep Learning -- aa) Nutzungsphase -- bb) Lernphase -- cc) Strukturelle Elemente -- 3. Expertensysteme -- 4. Maschinelles Planen und Handeln -- III. Kritische Eigenschaften von Systemen der Künstlichen Intelligenz -- 1. Schwer vorhersehbare Fehlermöglichkeiten beim Einsatz in komplexen Umgebungsszenarien -- 2. Fähigkeit zum autonomen Handeln -- 3. Einsatzbereiche mit erheblichem Einfluss auf Mensch und Umwelt -- 4. Geringer Grad der Transparenz und Nachvollziehbarkeit von Verhaltensregeln -- 5. Änderbarkeit der Verhaltensregeln im laufenden Betrieb -- 6. Beispiel "Autonomes Fahren" -- IV. Heutige Möglichkeiten und Grenzen von KI -- 1. Automatisierung geistiger und physischer Arbeit -- 2. "Übermenschliche" kognitive Einzelleistungen.

Das Buch beschäftigt sich mit der Möglichkeit der Prognose von Wohnungseinbrüchen mit Hilfe von Machine-Learning-Verfahren. Die Analyse beschränkt sich auf die Fragestellung, ob nach einem erfolgten Wohnungseinbruch mit einer Nachfolgetat, den sog. Near-Repeats, zu rechnen ist. Dabei wird untersucht, welche Faktoren zu guten Prognoseergebnissen beitragen. Zur Verfügung stehen Daten zu Wohnungseinbrüchen aus Baden-Württemberg aus den Jahren 2010 bis 2017. Ergänzt werden die polizeilichen Daten um geografische Daten, die den Tatort beschreiben. Hiermit wird geprüft, ob kriminalgeografische Faktoren gute Indikatoren zur Prognose von Wohnungseinbrüchen, genauer gesagt Near-Repeats, darstellen. Als Machine-Learning-Verfahren kommen die Verfahren Random Forest, XGBoost, Support Vector Machines, Neuronale Netze und ein Soft-Voting der Modelle zum Einsatz. Mit Hilfe dieser Verfahren kann eine Präzision der Prognosen von über 60% erreicht werden. Es wird außerdem erstmalig gezeigt, dass auch Prognosen für den ländlichen Raum möglich sind.

The last 25 years have seen a tremendous growth in the application of statistical and modelling techniques to ecological problems. This expansion has been accelerated by the increasing availability of software, books and computing power. However, the suitability of some of these approaches to data analysis, in a relatively knowledge-poor discipline such as ecology, can be questioned on grounds of appropriateness and robustness. One reason for these concerns is that many ecological problems are at best poorly defined and most lack algorithmic solutions. Machine learning methods offer the potential for a different approach to these difficult problems. One definition of machine learning is that it is concerned with inducing knowledge from data, where the data could be patterns in a game of chess or patterns in the species composition of natural communities. Unfortunately ecologists have little experience of these relatively recent and novel approaches to understanding data. This is a problem that is made more complex because there is no simple taxonomy of machine learning methods and there are relatively few examples in the mainstream ecological literature to encourage exploration. This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem. Examples include the identification of species, optimal mate choice, predicting species distributions and modelling landscape features. A group of experienced machine learning workers, who have become interested in environmental problems, have written a chapter that demonstrates how machine learning methods can be used to discover equations that describe the dynamic behaviour of ecological systems. The final chapter reviews `real learning', offering the potential for greater dialogue between the biological and machine learning communities

"Get to know the "why" and "how" of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, its a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. Gain a solid reason to use machine learning Frame your question using financial markets laws Know your data Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment and this book shows you how"--

Intro -- Preface -- Contents -- 1 Getting Started -- 1.1 Some Introductory Caveats -- 1.2 What Criminal Justice Risk Assessment Is and Is Not -- 1.3 Difficulties Defining Machine Learning -- 1.4 Setting the Stage -- 1.4.1 A Brief Motivating Example -- 2 Some Important Background Material -- 2.1 Policy Considerations -- 2.1.1 Criminal Justice Risk Assessment Goals -- 2.1.2 Decisions to Be Informed by the Forecasts -- 2.1.3 Outcomes to Be Forecasted -- 2.1.4 Real World Constraints -- 2.1.5 Stakeholders -- 2.2 Data Considerations -- 2.2.1 Stability Over Time -- 2.2.2 Training Data, Evaluation Data, and Test Data -- 2.2.3 Representative Data -- 2.2.4 Large Samples -- 2.2.5 Data Continuity -- 2.2.6 Missing Data -- 2.3 Statistical Considerations -- 2.3.1 Actuarial Methods -- 2.3.2 Building in the Relative Costs of Forecasting Errors -- 2.3.3 Effective Forecasting Algorithms -- 3 A Conceptual Introduction to Classification and Forecasting -- 3.1 Populations and Samples -- 3.2 Classification and Forecasting Using Decision Boundaries -- 3.3 Classification by Data Partitions -- 3.4 Forecasting by Data Partitions -- 3.5 Finding Good Data Partitions -- 3.6 Enter Asymmetric Costs -- 3.7 Recursive Partitioning Classification Trees -- 3.7.1 How Many Terminal Nodes? -- 3.7.2 Classification Tree Instability and Adaptive Fitting -- 4 A More Formal Treatment of Classification and Forecasting -- 4.1 Introduction -- 4.2 What Is Being Estimated? -- 4.3 Data Generation Formulations -- 4.4 Notation -- 4.5 From Probabilities to Classification -- 4.6 Computing (G|X) in the Real World -- 4.6.1 Estimation Bias -- 4.6.2 The Traditional Bias-Variance Tradeoff with Extensions -- 4.6.3 Addressing Uncertainty -- 4.7 A Bit More on the Joint Probability Model -- 4.8 Putting It All Together -- 5 Tree-Based Forecasting Methods -- 5.1 Introduction.