MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01857cam a22002178i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781108485067 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
004 |
| Item number |
BLU.F |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Blum, Avrim, |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Foundations of data science / |
| Statement of responsibility, etc. |
Avrim Blum, Toyota Technological Institute at Chicago, John Hopcroft, Cornell University, New York, Ravindran Kannan, Microsoft Research, India. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
UK |
| Name of publisher, distributor, etc. |
Cambridge University Press |
| Date of publication, distribution, etc. |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
424 pages |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 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"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Statistics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Quantitative research. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hopcroft, John E., |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kannan, Ravindran, |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
