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morik k machine learning under resource constraints vol 3 applications 2023

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Textbook in PDF format 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 3 describes how the resource-aware Machine Learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how Machine Learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine Learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from Machine Learning, for example by uncovering hidden characteristics of the wireless channel. Machine learning obtains predictions by constructing a predictive model. Many machine learning algorithms are built on black-box models, as opposed to statistical learning, which is more concerned with the statistical properties of the model, such as the distribution of the variables and its parameters. In this section, we focus on the problem of data- and dimensionality-reduction in Bayesian statistics. Bayesian regression does not assume a fixed optimal solution for a dataset as in the frequentist case, but introduces a distribution over the parameter space. The likelihood function models the information that comes from the data, and the prior distribution models problem-specific prior knowledge. Our goal is to explore and characterize the posterior distribution, which, as a consequence of Bayes’ theorem, is a compromise between the observed data situation and the prior knowledge that we assume for the parameters. For very large and high-dimensional datasets and settings where computational resources are scarce, the posterior distribution is hard to obtain. In general, our work focuses on algorithmic approaches that can be implemented in streaming and distributed environments to reduce the underlying problem in order to enhance the scalability of modern Bayesian regression approaches. Swarm algorithms create a group dynamic similar to the swarm behavior of animals, such as a flock of birds or a school of fish. Craig Reynolds developed a simulated swarm behavior in 1987 with the help of these observations. In logistics systems, a swarm algorithm is similar to traffic rules, which enable a smooth traffic flow by observing the local environment with as little communication as possible between the vehicles. One advantage of using swarm algorithms in logistics is that they do not require fixed routes but can use all available spaces. This allows them to easily change the layout and react to unpredictable events or disruptions. Furthermore, due to the modular design of swarm algorithms, new behaviors can be added without altering the existing ones. The paradigm of swarm behavior aims to enable several independent robots to collaborate towards achieving a collective goal, acting as a swarm. Contents: Editorial Health / Medicine Industry 4.0 Smart City and Traffic Communication Networks Privacy Bibliography Index Author information Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence at the TU Dortmund University. She is a pioneer of machine learning contributing substantially to inductive logic programming, support vector machines, probabilistic graphical models. In 2011, she acquired the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Data Analysis", of which she is the spokesperson. and computing architectures together so that machine learning models may be executed or even trained on resource restricted devices. It consists of 12 projects and a graduate school for more than 50 Ph. D. students. She is a spokesperson of the Competence Center for Machine Learning Rhein Ruhr (ML2R) and coordinator of the German competence centers for AI. She is the author of more than 200 publications in prestigious journals and conferences. She was a founding member, Program Chair and Vice Chair of the conference IEEE International Conference on Data Mining (ICDM) and is a member of the steering committee of and was Program Chair of ECML PKDD. Together with Volker Markl, Katharina Morik heads the working group "Technological Pioneers" of the platform "Learning Systems and Data Science" of the BMBF. Prof. Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences and Arts since 2016. She has been awarded Fellow of the German Society of Computer Science GI e.V. in 2019. Christian Wietfeld: Prof. Christian Wietfeld is head of the Chair of Communication Networks at TU Dortmund University since 2005. He graduated and received his doctorate from RWTH Aachen. His research interest focusses on future mobile communications networks, especially for safety-critical applications, e.g., in road traffic, logistics, energy technology, and robotics. His research work is documented in numerous publications as well as patents (13 international best paper awards, 5300+ citations). He has also co-founded several award-winning start-ups. Currently, the focus of his research is on 5G and 6G networks, e.g., in the 5G Competence Center funded by the state of North Rhine-Westphalia, where the potential of 5G technology for industrial production and other scenarios with particularly demanding requirements is being explored. Being the Co-Speaker of the Collaborative Research Center 876, he is investigating the use of novel artificial intelligence methods for reliable mobile networks of future generations. Since August 2021, he has been leading the activities of TU Dortmund University in the BMBF-funded 6G Research Hub 6GEM as site spokesperson. Jörg Rahnenführer: Prof. Dr. Jörg Rahnenführer is professor for "Statistical methods in genetics and chemometrics" at the Department of Statistics at TU Dortmund University. After obtaining a PhD in mathematics from the University of Düsseldorf he worked as a postdoc in Vienna, Berkeley, Omaha, and at the Max Planck Institute for Informatics in Saarbrücken. The respective departments covered a wide range of fields, including mathematics, statistics, biostatistics, genetics, and computer science. His group at TU Dortmund develops and applies in interdisciplinary projects statistical methods mainly for applications in bioinformatics, toxicology, and medicine. He has particularly worked successfully on the meaningful exploitation of high-dimensional omics data and as a member of the SFB 876 on hyperparameter optimization in statistical learning methods. Since 2021 he is the spokesperson of the DFG-funded Research Training Group (RTG) 2624 "Biostatistical Methods for High-Dimensional Data in Toxicology"

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morik k machine learning under resource constraints vol 3 applications 2023

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