Mathematical Foundations of Data Science
Heftet /
2024
/ Engelsk
Produktdetaljer
ISBN
9783031190766
Publisert
2024-03-14
Utgiver
Vendor
Springer International Publishing AG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Upper undergraduate, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Om bidragsyterne
Tomas Hrycej is a pioneer in the field of artificial intelligence and neural networks, having worked in this field since the 1980s. As an example of his pioneering deeds, he worked in the 1990s at Daimler Research on self-driving cars. In his doctoral thesis, he dealt with modular learning concepts in neural networks. His most important research stations were Daimler AG, Bosch GmbH, the University of Passau and currently the University of St. Gallen. He is the author of three monographs: Neurocontrol - Towards an Industrial Control Methodology, Modular Learning in Neural Networks (both Wiley-Interscience) and Robust Control ("Robuste Regelung", Springer), as well as about 60 publications in journals and conference proceedings.Bernhard Bermeitinger is a research assistant at the Chair of Data Science and Natural Language Processing and is currently working on his PhD in Deep Learning.
Siegfried Handschuh is a Full professor of Data Science and Natural Language Processing at the Institute of Computer Science at the University of St. Gallen, Switzerland. He received his PhD from the University of Karlsruhe (now: Karlsruhe Institute of Technology), Germany. His PhD thesis was in Collaboration with Stanford University as part of the American DARPA DAML project. Siegfried spend eight year in Ireland, where he led the Knowledge Discovery Unit at the Insight Centre for Data Analytics in Galway. He worked with multinational companies such as HP, SAP, IBM, Motorola and Elsevier Publishing. He also conducted research on the Digital Aristotle initiative, a project by Microsoft co-funder Paul Allen. He has published over 300 scientific papers and is highly citedwith an h-index of 41 (according to Google Scholar). This makes him one of the top-ranked Computer Scientists in Switzerland.
<p>“This is an excellent textbook in the field of big data processing and is highly recommended.” (Christian Posthoff, zbMATH 1529.68003, 2024)</p>
This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond” the sole computing experience.
Les mer
1. Data Science and its Tasks.- 2. Application Specific Mappings and Measuring the Fit to Data.- 3. Data Processing by Neural Networks.- 4. Learning and Generalization.- 5. Numerical Algorithms for Network Learning.- 6. Specific Problems of Natural Language Processing.- 7. Specific Problems of Computer Vision.
Les mer
Although it is widely recognized that analyzing large volumes of data by intelligent methods may provide highly valuable insights, the practical success of data science has led to the development of a sometimes confusing variety of methods, approaches and views.
This practical textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success.
Topics and features:
Focuses on approaches supported by mathematical arguments, rather thansole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parameterizationInvestigates the mathematical principles involved with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire book Although this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond” the sole computing experience.
Les mer
“This is an excellent textbook in the field of big data processing and is highly recommended.” (Christian Posthoff, zbMATH 1529.68003, 2024)
Offers a presentation structure aligned along key problems Focuses on approaches supported by mathematical arguments, rather than sole computing experiences Considers key data science problems and even explores natural language processing and computer vision
Les mer
Produktdetaljer
ISBN
9783031190766
Publisert
2024-03-14
Utgiver
Vendor
Springer International Publishing AG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Upper undergraduate, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Om bidragsyterne
Tomas Hrycej is a pioneer in the field of artificial intelligence and neural networks, having worked in this field since the 1980s. As an example of his pioneering deeds, he worked in the 1990s at Daimler Research on self-driving cars. In his doctoral thesis, he dealt with modular learning concepts in neural networks. His most important research stations were Daimler AG, Bosch GmbH, the University of Passau and currently the University of St. Gallen. He is the author of three monographs: Neurocontrol - Towards an Industrial Control Methodology, Modular Learning in Neural Networks (both Wiley-Interscience) and Robust Control ("Robuste Regelung", Springer), as well as about 60 publications in journals and conference proceedings.Bernhard Bermeitinger is a research assistant at the Chair of Data Science and Natural Language Processing and is currently working on his PhD in Deep Learning.
Siegfried Handschuh is a Full professor of Data Science and Natural Language Processing at the Institute of Computer Science at the University of St. Gallen, Switzerland. He received his PhD from the University of Karlsruhe (now: Karlsruhe Institute of Technology), Germany. His PhD thesis was in Collaboration with Stanford University as part of the American DARPA DAML project. Siegfried spend eight year in Ireland, where he led the Knowledge Discovery Unit at the Insight Centre for Data Analytics in Galway. He worked with multinational companies such as HP, SAP, IBM, Motorola and Elsevier Publishing. He also conducted research on the Digital Aristotle initiative, a project by Microsoft co-funder Paul Allen. He has published over 300 scientific papers and is highly citedwith an h-index of 41 (according to Google Scholar). This makes him one of the top-ranked Computer Scientists in Switzerland.