Foundations of Data Science / Avrim Blum
Material type:
TextPublisher number: :Variety Books Publishers & Distributors | :B-10 Street No 2 West Vinod Nagar Delhi 110092 Series: Taxts and Reading in MathematicsPublication details: New Delhi :Hindustan Book Agency 2020Description: xi,504p. 24cmISBN: 9789386279804Subject(s): Computer Science | Data processing and computer science | Statistics | Quantitative research | Big Data | Data ScienceDDC classification: 004 BLU | Item type | Current library | Call number | Status | Notes | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Mathematics Departmental Library
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SNU LIBRARY | 004 BLU (Browse shelf(Opens below)) | Available | FPDA Grant | M262 |
Browsing Mathematics Departmental Library shelves Close shelf browser (Hides shelf browser)
| 001.534 DEV Pattern recognition | 001.539 ASH Information Theory | 003.54 PAR Coding theorems of Classical and Quantum information Theory | 004 BLU Foundations of Data Science | 005.714 HAN Data Mining : concepts and techniques | 005.717 BAL A Course in Algebraic Error-Correcting Codes | 005.72 XAM Block Error-Correcting Codes |
"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"--
"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"--
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