“The book is full of cross-references, making the reader well aware of tight interconnections between many of the different approaches and methods. … the book is written in a very comprehensible and readable way. Its comprehensibility is further encreased through frequent marginal notes and through consistently illustrating all presented kinds of methods using the same toy example, and through historical notes to all addressed areas … the book explains also several quite advanced subjects … .” (Martin Holeňa, zbMATH 1479.68001, 2022)

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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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.
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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.
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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.
Les mer
“The book is full of cross-references, making the reader well aware of tight interconnections between many of the different approaches and methods. … the book is written in a very comprehensible and readable way. Its comprehensibility is further encreased through frequent marginal notes and through consistently illustrating all presented kinds of methods using the same toy example, and through historical notes to all addressed areas … the book explains also several quite advanced subjects … .” (Martin Holeňa, zbMATH 1479.68001, 2022)
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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 Request lecture materials: http://www.lamda.nju.edu.cn/yehj/mlbook/english/openaccess.html
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Produktdetaljer

ISBN
9789811519697
Publisert
2022-08-22
Utgiver
Vendor
Springer Verlag, Singapore
Høyde
240 mm
Bredde
168 mm
Aldersnivå
Upper undergraduate, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Orginaltittel
Machine Learning

Forfatter
Oversetter

Biographical note

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.