000			01653 a2200169 4500
020			_a9781108485067
082			_a004 _bBLU-F
100			_aBlum, Avrim
245			_aFoundations of Data Science
260			_aNew York _bCambridge University Press _c2020
300			_aviii;424 p
500			_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"-- Provided by publisher
504			_aIncludes bibliographical references and index.
650			_a Computer science. Statistics. Quantitative research.
700			_aHopcroft, John; Kannan, Ravindran
942			_cBK
999			_c26299 _d26299