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Artificial intelligence --- -AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Congresses --- -Congresses --- Knowledge Engineering --- Expert System

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Artificial intelligence. Robotics. Simulation. Graphics --- Machine learning --- Knowledge representation (Information theory)

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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

SCIENCE / Chemistry / General. --- Artificial Intelligence. --- Big Data and Machine Learning. --- Cyber-physical systems. --- Data mining for Ubiquitous System Software. --- Embedded Systems and Machine Learning. --- Highly Distributed Data. --- ML on Small devices. --- Machine learning for knowledge discovery. --- Machine learning in high-energy physics. --- Resource-Aware Machine Learning. --- Resource-Constrained Data Analysis.

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Die Autoren geben eine fundierte Einführung in die Informatik, die von Anfang an die Zusammenhänge zwischen den Teilgebieten des Faches betont. Das Buch ist kompakt, weil der gemeinsame Kern der verschiedenen Informatikgebiete betrachtet wird. In einer integrativen Sichtweise werden Modellierung, abstrakte Datentypen, Algorithmen sowie nebenläufige und verteilte Programmierung behandelt. Die grundlegenden Konzepte der Informatik werden dabei mittels der Programmiersprache Java realisiert. Wesentliches Anliegen der Autoren ist es, die Informatik als Wissenschaft der Abstraktion herauszustellen, und in diesem Sinne den Studierenden allgemeine Methoden zum Lösen praktischer Probleme zu vermitteln. Lernkontrollen und ein effektiver Index, der vor allem diejenigen Begriffe aufführt, die ein Informatiker einfach können muss, ermöglichen ein fokussiertes Studium. Ferner stehen vielfältige Programm-Beispiele im Internet bereit.

Software engineering. --- Computer programming. --- Software Engineering/Programming and Operating Systems. --- Programming Techniques. --- Software Engineering.