000			02784cam a22003618i 4500
001			21176097
003			IIITD
005			20230917020003.0
008			190829s2020 nyu b 001 0 eng
010			_a 2019038133
020			_a9781108485067
040			_aDLC _beng _erda _cDLC _dDLC
042			_apcc
050	0	0	_aQA76 _b.B5675 2020
082	0	0	_a004 _223 _bBLU-F
100	1		_aBlum, Avrim
245	1	0	_aFoundations of data science _cby Avrim Blum, John Hopcroft and Ravindran Kannan
260			_aNew York : _bCambridge University Press, _c©2020
263			_a1912
300			_aviii, 424 p. : _bill. ; _c26 cm.
504			_aIncludes bibliographical references and index.
505			_t1 Introduction _t2 High-Dimensional Space _t3 Best-Fit Subspaces and Singular Value Decomposition (SVD) _t4 Random Walks and Markov Chains _t5 Machine Learning _t6 Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling _t7 Clustering _t8 Random Graphs _t9 Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models _t10 Other Topics _t11 Wavelets _t12 Background Material
520			_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"--
650		0	_aComputer science.
650		0	_aStatistics.
650		0	_aQuantitative research.
700	1		_aHopcroft, John.
700	1		_aKannan, Ravindran
776	0	8	_iOnline version: _aBlum, Avrim, 1966- _tFoundations of data science _bFirst edition. _dNew York, NY : Cambridge University Press, 2020. _z9781108755528 _w(DLC) 2019038134
906			_a7 _brip _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _02
999			_c171357 _d171357