MARC details
| 000 -LEADER |
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| fixed length control field |
01954nam a22001697a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20211208161707.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
211208b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789386279804 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
004 |
| Item number |
BLU |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Blum, Avrim, |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Foundations of data science |
| Statement of responsibility, etc. |
Avrim Blum |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
NEW DELHI |
| Name of publisher, distributor, etc. |
Hindustan |
| Date of publication, distribution, etc. |
©2020 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Page number |
504p; |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Introduction --<br/> |
| -- |
High-dimensional space --<br/> |
| -- |
Best-fit subspaces and Singular Value Decomposition (SVD) --<br/> |
| -- |
Random walks and Markov chains --<br/> |
| -- |
Machine learning --<br/> |
| -- |
Algorithms for massive data problems: streaming, sketching, and sampling --<br/> |
| -- |
Clustering --<br/> |
| -- |
Random graphs --<br/>- |
| -- |
Topic models, non-negative matrix factorization, hidden Markov models, and graphical models --<br/> |
| -- |
Other topics --<br/> |
| -- |
Wavelets - |
| 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 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) |
|---|
| -- |
4551 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) |
|---|
| -- |
4552 |
