000			01649nam a22001817a 4500
005			20250501102904.0
008			250501b2020 |||||||| |||| 00| 0 eng d
020			_a9781108485067
082			_a004 BLU-A
100			_aBlum, Avrim
245			_aFoundations of data science / _cAvrim Blum, John Hopcroft and Ravindran Kannan
260			_aIndia _bCambridge University Press _c2020
300			_a424p.
500			_aThis 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			_aComputer Science
650			_aStatistics
650			_aQuantitative research.
999			_c93465 _d93465