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Mathematical foundations of data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical foundations of data scienceby Tomas Hrycej ... [et al.].
other author:	Hrycej, Tomas.
Published:	Cham :Springer International Publishing :2023.
Description:	xiii, 213 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer scienceMathematics.
Online resource:	https://doi.org/10.1007/978-3-031-19074-2
ISBN:	9783031190742$q(electronic bk.)

Mathematical foundations of data science
Mathematical foundations of data science[electronic resource] /by Tomas Hrycej ... [et al.]. - Cham :Springer International Publishing :2023. - xiii, 213 p. :ill., digital ;24 cm. - Texts in computer science,1868-095X. - Texts in computer science..

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.

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 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.

ISBN: 9783031190742$q(electronic bk.)

Standard No.: 10.1007/978-3-031-19074-2doiSubjects--Topical Terms:

181991
Computer science
--Mathematics.

LC Class. No.: QA76.9.M35

Dewey Class. No.: 004.0151

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520 $a 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 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.
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