Central Library

Machine Learning Foundations (Record no. 78342)

000 -LEADER
fixed length control field 03727nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-030-65900-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801220228.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210212s2021 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783030659004
-- 978-3-030-65900-4
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
100 1# - AUTHOR NAME
Author Jo, Taeho.
245 10 - TITLE STATEMENT
Title Machine Learning Foundations
Sub Title Supervised, Unsupervised, and Advanced Learning /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XX, 391 p. 277 illus., 13 illus. in color.
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Part I. Foundation -- Chapter 1. Introduction -- Chapter 2. Numerical Vectors -- Chapter 3.Data Encoding -- Chapter 4. Simple Machine Learning Algorithms -- Part II. Supervised Learning -- Chapter 5. Instance based Learning -- Chapter 6. Probabilistic Learning -- Chapter 7. Decision Tree -- Chapter 8. Support Vector Machine -- Part III. Unsupervised Learning -- Chapter 9. Simple Clustering Algorithms -- Chapter 10. K Means Algorithm -- Chapter 11. EM Algorithm -- Chapter 12. Advanced Clustering -- Part IV. Advanced Topics -- Chapter 13. Ensemble Learning -- Chapter 14. Semi-Supervised Learning -- Chapter 15. Temporal Learning -- Chapter 16. Reinforcement Learning.
520 ## - SUMMARY, ETC.
Summary, etc This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-030-65900-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2021.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Telecommunication.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information storage and retrieval systems.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Quantitative research.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information Storage and Retrieval.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Analysis and Big Data.
912 ## -
-- ZDB-2-ENG
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-- ZDB-2-SXE

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