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Foundations of data science

Blum, Avrim (1966-)

Foundations of data science

紀錄類型:	書目-語言資料,印刷品 : 單行本
作者:	BlumAvrim, 1966-
其他作者:	HopcroftJohn E., 1939-
其他作者:	KannanRavindran, 1953-
出版地:	Cambridge, United Kingdom
出版者:	Cambridge University Press;
出版年:	c2020.
面頁冊數:	viii, 424 p.ill. : 26 cm.;
標題:	Computer science. -
標題:	Statistics. -
標題:	Quantitative research. -
摘要註:	"This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data."--Back cover.
ISBN:	9781108485067

Foundations of data science
Blum, Avrim

Foundations of data science / Avrim Blum, John Hopcroft, Ravi Kannan. - Cambridge, United Kingdom : Cambridge University Press, c2020.. - viii, 424 p. ; ill. ; 26 cm..
ISBN 9781108485067ISBN 1108485065
Computer science.Statistics.Quantitative research.

Hopcroft, John E.

Foundations of data science
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210 $a Cambridge, United Kingdom $a New York, USA $c Cambridge University Press $d c2020.
215 1 $a viii, 424 p. $c ill. $d 26 cm.
330 $a "This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data."--Back cover.
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