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Mathematical Foundations of Data Science / by Tomas Hrycej, Bernhard Bermeitinger, Matthias Cetto, Siegfried Handschuh
(Texts in Computer Science. ISSN:1868095X)
| データ種別 | 電子ブック |
|---|---|
| 版 | 1st ed. 2023. |
| 出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
| 出版年 | 2023 |
| 大きさ | XIII, 213 p. 108 illus., 98 illus. in color : online resource |
| 著者標目 | *Hrycej, Tomas author Bermeitinger, Bernhard author Cetto, Matthias author Handschuh, Siegfried author SpringerLink (Online service) |
書誌詳細を非表示
| 一般注記 | 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 HTTP:URL=https://doi.org/10.1007/978-3-031-19074-2 |
|---|---|
| 件 名 | LCSH:Artificial intelligence -- Data processing
全ての件名で検索
LCSH:Computer science -- Mathematics 全ての件名で検索 LCSH:Discrete mathematics LCSH:Computer arithmetic and logic units FREE:Data Science FREE:Discrete Mathematics in Computer Science FREE:Mathematical Applications in Computer Science FREE:Arithmetic and Logic Structures |
| 分 類 | LCC:Q336 DC23:005.7 |
| 書誌ID | EB00003761 |
| ISBN | 9783031190742 |
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