Introduction to machine learning / (Record no. 1970)
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000 -LEADER | |
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fixed length control field | 02997cam a22002895i 4500 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | EG-CaNGU |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240131123917.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 231121t2020 maua frb 001 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9780262043793 |
Qualifying information | (hardcover) |
040 ## - CATALOGING SOURCE | |
Original cataloging agency | DLC |
Language of cataloging | eng |
Transcribing agency | DLC |
Description conventions | rda |
Modifying agency | DLC |
-- | EG-CaNGU |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.31 |
Item number | ALI |
Edition number | 23 |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Personal name | Alpaydin, Ethem, |
Relator term | author. |
9 (RLIN) | 5913 |
245 10 - TITLE STATEMENT | |
Title | Introduction to machine learning / |
250 ## - EDITION STATEMENT | |
Edition statement | Fourth edition. |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Place of production, publication, distribution, manufacture | Cambridge, Massachusetts : |
Name of producer, publisher, distributor, manufacturer | The MIT Press, |
Date of production, publication, distribution, manufacture, or copyright notice | [2020] |
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Date of production, publication, distribution, manufacture, or copyright notice | ©2020 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xxiv, 682 pages : |
Other physical details | illustrations ; |
Dimensions | 24 cm. |
336 ## - CONTENT TYPE | |
Content type term | text |
Content type code | txt |
Source | rdacontent |
337 ## - MEDIA TYPE | |
Media type term | unmediated |
Media type code | n |
Source | rdamedia |
338 ## - CARRIER TYPE | |
Carrier type term | volume |
Carrier type code | nc |
Source | rdacarrier |
490 0# - SERIES STATEMENT | |
Series statement | Adaptive computation and machine learning series |
504 ## - BIBLIOGRAPHY, ETC. NOTE | |
Bibliography, etc. note | Includes bibliographical references and index. |
505 00 - FORMATTED CONTENTS NOTE | |
Formatted contents note | Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering --Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments. |
520 ## - SUMMARY, ETC. | |
Summary, etc. | "Since the third edition of this text appeared in 2014, most recent advances in machine learning, both in theory and application, are related to neural networks and deep learning. In this new edition, the author has extended the discussion of multilayer perceptrons. He has also added a new chapter on deep learning including training deep neural networks, regularizing them so they learn better, structuring them to improve learning, e.g., through convolutional layers, and their recurrent extensions with short-term memory necessary for learning sequences. There is a new section on generative adversarial networks that have found an impressive array of applications in recent years. Alpaydin has also extended the chapter on reinforcement learning to discuss the use of deep networks in reinforcement learning. There is a new section on the policy gradient method that has been used frequently in recent years with neural networks, and two additional sections on two examples of deep reinforcement learning, which both made headlines when they were announced in 2015 and 2016 respectively. One is a network that learns to play arcade video games, and the other one learns to play Go. There are also revisions in other chapters reflecting new approaches, such as embedding methods for dimensionality reduction, and multi-label classification. In response to requests from instructors, this new edition contains two new appendices on linear algebra and optimization, to remind the reader of the basics of those topics that find use in machine learning"-- |
Assigning source | Provided by publisher. |
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Machine learning. |
Source of heading or term | NGU-sh |
9 (RLIN) | 3897 |
Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Arrivals Code | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
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Dewey Decimal Classification | Not For Loan | Information Technology | Library D | Library D | 11/22/2023 | Baccah | 3910.00 | ITS202311 | 006.31 ALI | 1004541 | 08/14/2024 | 11/22/2023 | Book Non-borrowing | |||
Dewey Decimal Classification | Information Technology | Library D | Library D | 11/22/2023 | Baccah | 3910.00 | ITS202311 | 006.31 ALI | 1004542 | 08/14/2024 | 11/22/2023 | Book |