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A01=Edgar A. Levenson
Age Group_Uncategorized
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Author_Edgar A. Levenson
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B01=Alan Slomowitz
B01=Katharina Morik
B01=Peter Marwedel
Category1=Non-Fiction
Category=TGPC
Category=UB
Category=UNF
Category=UYQM
COP=Germany
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Language_English
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Machine Learning under Resource Constraints - Fundamentals

English

By (author): Edgar A. Levenson

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 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

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A01=Edgar A. LevensonAge Group_UncategorizedAuthor_Edgar A. Levensonautomatic-updateB01=Alan SlomowitzB01=Katharina MorikB01=Peter MarwedelCategory1=Non-FictionCategory=TGPCCategory=UBCategory=UNFCategory=UYQMCOP=GermanyDelivery_Pre-orderLanguage_EnglishPA=Temporarily unavailablePrice_€100 and abovePS=Activesoftlaunch

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Product Details
  • Weight: 843g
  • Dimensions: 170 x 240mm
  • Publication Date: 31 Dec 2022
  • Publisher: De Gruyter
  • Publication City/Country: Germany
  • Language: English
  • ISBN13: 9783110785937

About Edgar A. Levenson

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. Dr. Peter Marwedel studied physics at the University of Kiel Germany. He received his PhD in physics in 1974. As a post-doc he published some of the first papers on high-level synthesis and retargetable compilation in the context of the MIMOLA hardware description language. In 1987 his habilitation thesis in computer science was accepted. He worked as a professor for computer engineering at TU Dortmund since 1989. He is chairing ICD a local spin-off of TU Dortmund. His research interests include design automation for embedded systems in particular the generation of efficient embedded software. Focus is on energy efficiency and timing predictability. Dr. Marwedel published papers on energy-efficient and timing-predictable software including compiler-supported use of scratchpad memories. He is the author of one of the few textbooks on embedded systems. The book is complemented by videos available on youtube and by publicly available slides. He served as the vice-chair of the collaborative research center SFB 876 aiming at resource-efficient analysis of large data sets since 2011. Dr. Marwedel is an IEEE Fellow. He received the EDAA Lifetime Achievement Award in 2013 and the ESWEEK Lifetime achievement award in 2014.

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