Siegfried Handschuh – författare

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.

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 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.

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.

This book constitutes the refereed proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, held in Passau, Germany, in June 2015. The 18 full papers, 15 short papers, 14 poster and demonstration papers presented were carefully reviewed and selected from 100 submissions. The papers cover the following topics: information extraction, distributional semantics, querying and question answering systems, context-aware NLP, cognitive and semantic computing, sentiment and opinion analysis, information extraction and social media, NLP and usability, text classification and extraction, and posters and demonstrations.

The Semantic Web is one of the major current endeavours of applied computer science. The Semantic Web aims at enriching the existing Web with meta-data and processing methods so as to provide Web-based systems with advanced (- called intelligent) capabilities, in particular with context-awarenessand decision support. TheadvancedcapabilitiesrequiredinmostSemanticWebapplicationscen- ios primarily call for reasoning. Reasoning capabilities are o?ered by Semantic Web languages that are currently being developed. Most of these languages, however,are developed mainly from functionality-centred perspectives (e.g., - tology reasoning or access validation) or application-centred perspectives (e.g., Web service retrieval and composition). A perspective centred on the reasoning techniques complementing the above-mentioned activities appears desirable for Semantic Web systems and applications. The Summer School is devoted to this perspective. The "ReasoningWeb" series of annual Summer Schools was started in 2005 on behalf of the work package "Education and Training (ET)" of the Network of Excellence REWERSE.This year's edition focused on the use of semantic technologies to enhance data access on the Web. For this reason, courses presented a range of techniques and formalisms which bridge semantic-based and data-intensive systems.

The Semantic Web aims at enriching the existing Web with meta-data and processing methods so as to provide web-based systems with advanced capabilities, in particular with context awareness and decision support. The objective of this book is to provide a coherent introduction to semantic web methods and research issues with a particular emphasis on reasoning. The 7th reasoning web Summer School, held in August 2011, focused on the central topic of applications of reasoning for the emerging “Web of Data”. The 12 chapters in the present book provide excellent educational material as well as a number of references for further reading. The book not only addresses students working in the area, but also those seeking an entry point to various topics related to reasoning over Web data.

This book constitutes the refereed proceedings of the 18th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2012, held in Galway City, Ireland, in October 2012. The 44 revised full papers were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections on knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, application of knowledge engineering, and demonstrations.

This book constitutes the refereed proceedings of the 18th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2012, held in Galway City, Ireland, in October 2012. The 44 revised full papers were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections on knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, application of knowledge engineering, and demonstrations.

Generative Künstliche Intelligenz beschreibt eine Klasse von KI-Systemen, die in der Lage sind, aus großen Datenmengen zu lernen und auf dieser Grundlage neue, bisher nicht gesehene Inhalte zu generieren, wie beispielsweise Texte, Bilder, Musik oder Videos. Dabei wird die Generierungskapazität der KI mit dem Ziel eingesetzt, kreative Prozesse zu unterstützen, neue Ideen zu generieren und innovative Lösungsansätze zu liefern. Trotz ihrer beeindruckenden Fähigkeiten haben generative KI-Systeme auch ihre Herausforderungen, wie die Kontrolle über den generierten Inhalt, das Verständnis von Kontext und Bedeutung sowie ethische Fragen​ im Zusammenhang mit der Nutzung von generativer KI. Der Band gibt einen Überblick über generative KI-Systeme und beleuchtet die Auswirkungen auf das Management von Innovationen, Wirtschaft, Bildung und Gesellschaft.​