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Machine Learning

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Machine Learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learningby Zhi-Hua Zhou.
Author:	Zhou, Zhi-Hua.
Published:	Singapore :Springer Singapore :2021.
Description:	xiii, 459 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-15-1967-3
ISBN:	9789811519673

Machine Learning
Zhou, Zhi-Hua.

Machine Learning[electronic resource] /by Zhi-Hua Zhou. - Singapore :Springer Singapore :2021. - xiii, 459 p. :ill., digital ;24 cm.

1 Introduction -- 2 Model Selection and Evaluation -- 3 Linear Models -- 4 Decision Trees -- 5 Neural Networks -- 6 Support Vector Machine -- 7 Bayes Classifiers -- 8 Ensemble Learning -- 9 Clustering -- 10 Dimensionality Reduction and Metric Learning -- 11 Feature Selection and Sparse Learning -- 12 Computational Learning Theory -- 13 Semi-Supervised Learning -- 14 Probabilistic Graphical Models -- 15 Rule Learning -- 16 Reinforcement Learning.

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. 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.

ISBN: 9789811519673

Standard No.: 10.1007/978-981-15-1967-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .Z56 2021

Dewey Class. No.: 006.31

Machine Learning
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245 1 0 $a Machine Learning $h [electronic resource] / $c by Zhi-Hua Zhou.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xiii, 459 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 Introduction -- 2 Model Selection and Evaluation -- 3 Linear Models -- 4 Decision Trees -- 5 Neural Networks -- 6 Support Vector Machine -- 7 Bayes Classifiers -- 8 Ensemble Learning -- 9 Clustering -- 10 Dimensionality Reduction and Metric Learning -- 11 Feature Selection and Sparse Learning -- 12 Computational Learning Theory -- 13 Semi-Supervised Learning -- 14 Probabilistic Graphical Models -- 15 Rule Learning -- 16 Reinforcement Learning.
520 $a 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. 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 0 $a Machine learning. $3 188639
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Mathematics of Computing. $3 273710
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856 4 0 $u https://doi.org/10.1007/978-981-15-1967-3
950 $a Computer Science (SpringerNature-11645)