| 000 -LEADER |
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| fixed length control field |
04462nam a2200637 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KOHA |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20231016174104.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
KOHA |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220514s2020 maud 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780262043793 |
| Qualifying information |
(hardback) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
TR-IsMEF |
| Language of cataloging |
eng |
| Transcribing agency |
OZU |
| Modifying agency |
T9K |
| -- |
TR-IsMEF |
| Description conventions |
rda |
| 041 0# - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 |
| Item number |
.A46 2020 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Alpaydın, Ethem, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Introduction to machine learning / |
| Statement of responsibility, etc. |
Ethem Alpaydın |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
Fourth edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Cambridge, Massaschusetts : |
| Name of producer, publisher, distributor, manufacturer |
The MIT Press, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxiv, 682 pages : |
| Other physical details |
charts ; |
| Dimensions |
24 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Adaptive Computation and Machine Learning Series. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index (pages 673-682). |
| 520 0# - SUMMARY, ETC. |
|---|
| Summary, etc |
Available at a lower price from other sellers that may not offer free Prime shipping.<br/>A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.<br/>The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.<br/><br/>The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.--backover. |
| Uniform Resource Identifier |
<a href="https://www.amazon.com/Introduction-Machine-Learning-Adaptive-Computation/dp/0262043793">https://www.amazon.com/Introduction-Machine-Learning-Adaptive-Computation/dp/0262043793</a> |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Linear discrimination |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Title of a work |
Adaptive Computation and Machine Learning Series. |
| 900 ## - EQUIVALENCE OR CROSS-REFERENCE-PERSONAL NAME [LOCAL, CANADA] |
|---|
| Personal name |
MEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules |
| 910 ## - USER-OPTION DATA (OCLC) |
|---|
| User-option data |
Pandora |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Books |
| Koha issues (borrowed), all copies |
1 |
| 970 12 - REFERENCES |
|---|
| Chapters |
1 |
| Title of a work |
Introduction, |
| Pages |
1. |
| 970 12 - REFERENCES |
|---|
| Chapters |
2 |
| Title of a work |
Supervised Learning, |
| Pages |
23. |
| 970 12 - REFERENCES |
|---|
| Chapters |
3 |
| Title of a work |
Bayesian Decision Theory, |
| Pages |
51. |
| 970 12 - REFERENCES |
|---|
| Chapters |
4 |
| Title of a work |
Parametric Methods, |
| Pages |
67. |
| 970 12 - REFERENCES |
|---|
| Chapters |
5 |
| Title of a work |
Multivariate Methods, |
| Pages |
95. |
| 970 12 - REFERENCES |
|---|
| Chapters |
6 |
| Title of a work |
Dimensionality Reduction, |
| Pages |
117. |
| 970 12 - REFERENCES |
|---|
| Chapters |
7 |
| Title of a work |
Clustering, |
| Pages |
165. |
| 970 12 - REFERENCES |
|---|
| Chapters |
8 |
| Title of a work |
Nonparametric Methods, |
| Pages |
189. |
| 970 12 - REFERENCES |
|---|
| Chapters |
9 |
| Title of a work |
Decision Trees, |
| Pages |
217. |
| 970 12 - REFERENCES |
|---|
| Chapters |
10 |
| Title of a work |
Linear Discrimination, |
| Pages |
243. |
| 970 12 - REFERENCES |
|---|
| Chapters |
11 |
| Title of a work |
Multilayer Perceptrons, |
| Pages |
271. |
| 970 12 - REFERENCES |
|---|
| Chapters |
12 |
| Title of a work |
Deep Learning, |
| Pages |
313. |
| 970 12 - REFERENCES |
|---|
| Chapters |
13 |
| Title of a work |
Local Models, |
| Pages |
361. |
| 970 12 - REFERENCES |
|---|
| Chapters |
14 |
| Title of a work |
Kernel Machines, |
| Pages |
395. |
| 970 12 - REFERENCES |
|---|
| Chapters |
15 |
| Title of a work |
Graphical Models, |
| Pages |
433. |
| 970 12 - REFERENCES |
|---|
| Chapters |
16 |
| Title of a work |
Hidden Markov Models, |
| Pages |
463. |
| 970 12 - REFERENCES |
|---|
| Chapters |
17 |
| Title of a work |
Bayesian Estimation, |
| Pages |
491. |
| 970 12 - REFERENCES |
|---|
| Chapters |
18 |
| Title of a work |
Combining Multiple Learners, |
| Pages |
533. |
| 970 12 - REFERENCES |
|---|
| Chapters |
19 |
| Title of a work |
Reinforcement Learning, |
| Pages |
563. |
| 970 12 - REFERENCES |
|---|
| Chapters |
20 |
| Title of a work |
Design and Analysis of Machine Learning Experiments, |
| Pages |
597. |
| 970 11 - REFERENCES |
|---|
| Chapters |
A |
| Title of a work |
Probability, |
| Pages |
643. |
| 970 11 - REFERENCES |
|---|
| Chapters |
B |
| Title of a work |
Linear Algebra, |
| Pages |
655. |
| 970 11 - REFERENCES |
|---|
| Chapters |
C |
| Title of a work |
Optimization, |
| Pages |
665. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| -- |
42304 |