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008			230313s2023 sz | o |||| 0|eng d
020			_a9783031190742
024	7		_a10.1007/978-3-031-19074-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .M35 _b2023 EB
100	1		_aHrycej, Tomas, _eautor _4http://id.loc.gov/vocabulary/relators/aut _964073 _d1954-
245	1	0	_aMathematical Foundations of Data Science _cby Tomas Hrycej, Bernhard Bermeitinger, Matthias Cetto, Siegfried Handschuh
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aTexts in Computer Science _x1868-095X
505	0		_a1. 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.
520			_aAlthough 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9139268 _aInformática _xMatemáticas
650		7	_2embne _9495511 _aDatos masivos
700	1		_9689691 _aBermeitinger, Bernhard _eautor
700	1		_9689692 _aCetto, Matthias _eautor
700	1		_9689693 _aHandschuh, Siegfried _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-19074-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eIG _zSI