Machine Learning under Resource Constraints - Fundamentals

Katharina Morik, Peter Marwedel, Jens Buss , Andreas Becker

$304.95 $243.87

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

De Gruyter
31 December 2022

Computer aided manufacture (CAM); Computing: general; Data mining; Machine learning

Series: De Gruyter STEM

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.

Edited by: Katharina Morik, Peter Marwedel, Jens Buss, Andreas Becker
Imprint: De Gruyter
Country of Publication: Germany
Dimensions: Height: 240mm, Width: 170mm,
Weight: 843g
ISBN: 9783110785937
ISBN 10: 3110785935
Series: De Gruyter STEM
Pages: 505
Publication Date: 31 December 2022
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

"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."