Mathematical Foundations of Data Science (Texts in Computer Science) - Tapa dura

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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.

De la contraportada

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. 

Topics and features:

• Focuses on approaches supported by mathematical arguments, rather than sole computing experiences
• Investigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from them
• Considers 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 algorithms
• Examines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problem
• Addresses the trade-off between model size and volume of data available for its identification and its consequences for model parameterization
• Investigates the mathematical principles involved with natural language processing and computer vision
• Keeps 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.