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Machine Learning
Buch von Zhi-Hua Zhou
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

Zhi-Hua Zhou is a leading expert on machine learning and artificial intelligence. He is currently a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and the founding director of the LAMDA Group at Nanjing University, China. Prof. Zhou has authored the books "Ensemble Methods: Foundations and Algorithms" (2012) and "Machine Learning" (in Chinese, 2016), and published more than 200 papers in top-tier international journals and conferences. He founded the ACML (Asian Conference on Machine Learning), and served as chairperson for many prestigious conferences, including AAAI 2019 program chair, ICDM 2016 general chair, IJCAI 2015 machine learning track chair, and area chair for NeurIPS, ICML, AAAI, IJCAI, KDD, etc. He is editor-in-chief of Frontiers of Computer Science, and has been an associate editor for prestigious journals such as the Machine Learning journal and IEEE PAMI. He is a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF and CAAI, and recipient of numerous awards, including the National Natural Science Award of China and the IEEE Computer Society Edward J. McCluskey Technical Achievement Award.

Zusammenfassung

Provides a comprehensive and unbiased introduction to almost all aspects of machine learning

Received a Chinese literature prize for its elegant presentation

The Chinese version has sold 200,000+ copies

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2021
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xiii
459 S.
69 s/w Illustr.
68 farbige Illustr.
459 p. 137 illus.
68 illus. in color.
ISBN-13: 9789811519666
ISBN-10: 9811519668
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Zhou, Zhi-Hua
Übersetzung: Liu, Shaowu
Auflage: 1st ed. 2021
Hersteller: Springer Singapore
Springer Nature Singapore
Maße: 246 x 173 x 31 mm
Von/Mit: Zhi-Hua Zhou
Erscheinungsdatum: 21.08.2021
Gewicht: 0,962 kg
Artikel-ID: 117658851
Über den Autor

Zhi-Hua Zhou is a leading expert on machine learning and artificial intelligence. He is currently a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and the founding director of the LAMDA Group at Nanjing University, China. Prof. Zhou has authored the books "Ensemble Methods: Foundations and Algorithms" (2012) and "Machine Learning" (in Chinese, 2016), and published more than 200 papers in top-tier international journals and conferences. He founded the ACML (Asian Conference on Machine Learning), and served as chairperson for many prestigious conferences, including AAAI 2019 program chair, ICDM 2016 general chair, IJCAI 2015 machine learning track chair, and area chair for NeurIPS, ICML, AAAI, IJCAI, KDD, etc. He is editor-in-chief of Frontiers of Computer Science, and has been an associate editor for prestigious journals such as the Machine Learning journal and IEEE PAMI. He is a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF and CAAI, and recipient of numerous awards, including the National Natural Science Award of China and the IEEE Computer Society Edward J. McCluskey Technical Achievement Award.

Zusammenfassung

Provides a comprehensive and unbiased introduction to almost all aspects of machine learning

Received a Chinese literature prize for its elegant presentation

The Chinese version has sold 200,000+ copies

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2021
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xiii
459 S.
69 s/w Illustr.
68 farbige Illustr.
459 p. 137 illus.
68 illus. in color.
ISBN-13: 9789811519666
ISBN-10: 9811519668
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Zhou, Zhi-Hua
Übersetzung: Liu, Shaowu
Auflage: 1st ed. 2021
Hersteller: Springer Singapore
Springer Nature Singapore
Maße: 246 x 173 x 31 mm
Von/Mit: Zhi-Hua Zhou
Erscheinungsdatum: 21.08.2021
Gewicht: 0,962 kg
Artikel-ID: 117658851
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