滿額折
機器學習的綜合基礎（簡體書）
商品資訊
ISBN13：9787560660547
替代書名：Machine Learning――A Comprehensive Foundation
出版社：西安電子科技大學出版社
作者：張軍英
出版日：2021/08/02
裝訂：平裝
商品簡介
商品簡介
This book provides a comprehensive foundation of machine learning. To answer the
questions of what to learn, how to learn, what to get from learning, and how to evaluate,
as well as what is meant by learning, the book focuses on the fundamental basics
of machine learning, its methodology, theory, algorithms, and evaluations, together
with some philosophical thinking on comparison between machine learning and human
learning for machinery intelligence.
The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2),
Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6),
Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble
learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and
Challenges (Chapter 12).
The book can be used as a textbook for college, undergraduate, graduate and PhD
students majored in computer science, automation, electronic engineering,
communication, ect. It can also be used as a reference for readers who are
interested in machine learning and hope to make contributions to the field.
questions of what to learn, how to learn, what to get from learning, and how to evaluate,
as well as what is meant by learning, the book focuses on the fundamental basics
of machine learning, its methodology, theory, algorithms, and evaluations, together
with some philosophical thinking on comparison between machine learning and human
learning for machinery intelligence.
The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2),
Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6),
Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble
learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and
Challenges (Chapter 12).
The book can be used as a textbook for college, undergraduate, graduate and PhD
students majored in computer science, automation, electronic engineering,
communication, ect. It can also be used as a reference for readers who are
interested in machine learning and hope to make contributions to the field.
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