Foundations of data science
Blum, Avrim
Foundations of data science by Avrim Blum, John Hopcroft and Ravindran Kannan - New York : Cambridge University Press, ©2020 - viii, 424 p. : ill. ; 26 cm.
Includes bibliographical references and index.
1 Introduction
2 High-Dimensional Space 3 Best-Fit Subspaces and Singular Value Decomposition (SVD) 4 Random Walks and Markov Chains 5 Machine Learning 6 Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling 7 Clustering 8 Random Graphs 9 Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models 10 Other Topics 11 Wavelets
12 Background Material
"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"--
9781108485067
2019038133
Computer science.
Statistics.
Quantitative research.
QA76 / .B5675 2020
004 / BLU-F
Foundations of data science by Avrim Blum, John Hopcroft and Ravindran Kannan - New York : Cambridge University Press, ©2020 - viii, 424 p. : ill. ; 26 cm.
Includes bibliographical references and index.
1 Introduction
2 High-Dimensional Space 3 Best-Fit Subspaces and Singular Value Decomposition (SVD) 4 Random Walks and Markov Chains 5 Machine Learning 6 Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling 7 Clustering 8 Random Graphs 9 Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models 10 Other Topics 11 Wavelets
12 Background Material
"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"--
9781108485067
2019038133
Computer science.
Statistics.
Quantitative research.
QA76 / .B5675 2020
004 / BLU-F