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Machine Learning

Zhou, Zhi-Hua.

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Machine Learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning/ by Zhi-Hua Zhou.
作者:	Zhou, Zhi-Hua.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xiii, 459 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	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:

533906
Machine learning.

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

Dewey Class. No.: 006.31

Machine Learning
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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.
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