000			02056nam a22002177a 4500
003			OSt
005			20240919144433.0
008			200320b ||||| |||| 00| 0 eng d
020			_a9789386279804
040			_cEducational Supplies _aICTS-TIFR
050			_aQA76.BLU
100			_aBlum, Avrim
245			_aFoundations of data science
260			_aNew Delhi: _bHindustan Book Agency, _c[c2020]
300			_a504 p
505			_a1 - Introduction 2 - High-Dimensional Space 3 - Best-Fit Subspaces and Singular Value Decomposition (SVD) 4 - Machine Learning 5 - Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling 6 - Clustering 7 - Random Graphs 8 - Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models 9 - Other Topics 10 - Wavelets
520			_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.
700			_aHopcroft, John E
700			_aKannan, Ravindran
942			_2lcc _cBK
999			_c3060 _d3060