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Machine Learning - 9789811519666

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Product Description

Machine LearningAuthor(s): Zhi-Hua Zhou, Shaowu Liu

Format: Hardback

Publisher: Springer Verlag, Singapore, Singapore

Imprint: Springer Verlag, Singapore

ISBN-13: 9789811519666, 978-9811519666

Synopsis

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 (Chapt.

Machine Learning - 9789811519666

Machine LearningAuthor(s): Zhi-Hua Zhou, Shaowu Liu

Format: Hardback

Publisher: Springer Verlag, Singapore, Singapore

Imprint: Springer Verlag, Singapore

ISBN-13: 9789811519666, 978-9811519666

Synopsis

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 (Chapt.

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