MARC details
000 -LEADER |
fixed length control field |
02264nam a22003017a 4500 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
OSt |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20241112143606.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
241112b |||||||| |||| 00| 0 hin d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9789811519697 |
040 ## - CATALOGING SOURCE |
Original cataloging agency |
NISER LIBRARY |
Language of cataloging |
eng |
Transcribing agency |
NISER LIBRARY |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
004.85 |
Item number |
ZHO-M |
100 ## - MAIN ENTRY--PERSONAL NAME |
Personal name |
Zhou, Zhi-hua |
245 ## - TITLE STATEMENT |
Title |
Machine learning |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
Place of publication, distribution, etc. |
Singapore : |
Name of publisher, distributor, etc. |
Springer, |
Date of publication, distribution, etc. |
2021 |
300 ## - PHYSICAL DESCRIPTION |
Extent |
xiii, 458p. |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc |
Includes index |
520 ## - SUMMARY, ETC. |
Summary, etc. |
Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. |
521 ## - TARGET AUDIENCE NOTE |
Target audience note |
The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Machine learning |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Neural networks |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Bayesian networks |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Learning algorithms |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Mathematical models |
700 ## - ADDED ENTRY--PERSONAL NAME |
Personal name |
Liu, Shaowu |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Materials specified |
Table of contents |
Uniform Resource Identifier |
<a href="https://link.springer.com/content/pdf/bfm:978-981-15-1967-3/1">https://link.springer.com/content/pdf/bfm:978-981-15-1967-3/1</a> |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Materials specified |
Reviews |
Uniform Resource Identifier |
<a href="https://www.goodreads.com/book/show/71469651-machine-learning#CommunityReviews">https://www.goodreads.com/book/show/71469651-machine-learning#CommunityReviews</a> |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Universal Decimal Classification |
Koha item type |
Book |