Foundations of data science (Record no. 3060)

000 -LEADER
fixed length control field 02056nam a22002177a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240919144433.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200320b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789386279804
040 ## - CATALOGING SOURCE
Transcribing agency Educational Supplies
Original cataloging agency ICTS-TIFR
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.BLU
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Blum, Avrim
245 ## - TITLE STATEMENT
Title Foundations of data science
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New Delhi:
Name of publisher, distributor, etc. Hindustan Book Agency,
Date of publication, distribution, etc. [c2020]
300 ## - Physical Description
Pages: 504 p
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 1 - Introduction <br/>2 - High-Dimensional Space <br/>3 - Best-Fit Subspaces and Singular Value Decomposition (SVD) <br/>4 - Machine Learning <br/>5 - Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling<br/>6 - Clustering <br/>7 - Random Graphs <br/>8 - Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models<br/>9 - Other Topics <br/>10 - Wavelets<br/><br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc. 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.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Hopcroft, John E
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Kannan, Ravindran
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Shelving location Date acquired Full call number Accession No. Koha item type
          ICTS Rack No 2 03/20/2020 QA76.BLU 02413 Book