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Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.

Machine learning --- Machine learning.

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Machine elements --- Machine learning.

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This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5. 2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters. 3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations. The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners.

Machine learning. --- Machine Learning.

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Das Verstehen menschlichen Verhaltens ist essenziell für intelligente technische Systeme in menschlichen Umgebungen. Diese Arbeit befasst sich mit der videobasierten Aktivitätsanalyse. Dazu werden zwei Methoden der Merkmalsextraktion untersucht: ein markerloses dreidimensionales Körpertracking mit einem evolutionären Algorithmus und ein modellfreies Tracking dynamischer Videomerkmale. Anschließend erfolgt eine Modellierung und Klassifikation von Aktivitäten auf Basis der gewonnenen Merkmale. Understanding human behavior is crucial for intelligent technical systems in human environments. In this work, methods for human activity recognition based on video data are developed. Two approaches of feature extraction are pursued: a method of markerless articulated pose estimation using an evolutionary algorithm and a model-free feature tracking method. The resulting motion representations are then used for modeling and classifying different activities.

Machine learning. --- Machine learning

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"The magazine of metalworking manufacturing."

Machine-tools --- Machine-tools.