Principles of Machine Learning: The Three Perspectives
Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.

The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled “Perspectives,” comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, “Frameworks”: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, “Paradigms,” encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, “Tasks”: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.

This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.

The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

1145833209
Principles of Machine Learning: The Three Perspectives
Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.

The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled “Perspectives,” comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, “Frameworks”: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, “Paradigms,” encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, “Tasks”: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.

This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.

The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

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Principles of Machine Learning: The Three Perspectives

Principles of Machine Learning: The Three Perspectives

by Wenmin Wang
Principles of Machine Learning: The Three Perspectives
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Principles of Machine Learning: The Three Perspectives

Principles of Machine Learning: The Three Perspectives

by Wenmin Wang

Overview

Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.

The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled “Perspectives,” comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, “Frameworks”: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, “Paradigms,” encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, “Tasks”: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.

This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.

The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

Product Details

ISBN-13: 9789819753321
Publisher: Springer Nature Singapore
Publication date: 10/27/2024
Edition description: 2024
Pages: 527
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Wenmin Wang is a professor and program director in the School of Computer Science and Engineering within the Faculty of Innovation Engineering at Macau University of Science and Technology (MUST), China, from 2019. Previous to the MUST, he held the position of professor and executive vice dean/dean with the School of Electronic and Computer Engineering at Peking University (PKU). In PKU, he taught a course on Principles of Artificial Intelligence to graduate students. And in MUST, he has been teaching the two compulsory courses Machine Learning and Principles of Artificial Intelligence to graduate students.

This book was started to be written after his Chinese edition of Principles of Artificial Intelligence was published by Higher Education Press (China) in August 2019. In recognition of his accomplishments in the online open course “Principles of Artificial Intelligence,” he was honored with the “National Excellent Online Open Course” Award by the Chinese Ministry of Education in 2018. Additionally, he was bestowed with the “Teaching Excellence Award” by PKU, in 2017. His journey into the field of artificial intelligence during his doctoral studies, culminated in his PhD thesis entitled A Member System Model Supporting AI Problem Solving. Then he received a PhD degree in computer science from Harbin Institute of Technology (HIT), China, in March 1989.

Table of Contents

Part I. Perspectives.- Chapter 1. Introduction.- Chapter 2. On Perspectives.- Part II. Frameworks. Chapter 3. Probabilistic Framework.- Chapter 4. Statistical Framework.- Chapter 5. Connectionist Framework.- Chapter 6. Symbolic Framework.- Chapter 7. Behavioral Framework.- Part III. Paradigms.- Chapter 8. Supervised Learning Paradigm.- Chapter 9. Unsupervised Learning Paradigm.- Chapter 10. Reinforcement Learning Paradigm.- Chapter 11. Other Learning Quasi-Paradigm.- Part IV. Tasks.- Chapter 12. Classification Task.- Chapter 13. Regression Task.- Chapter 14. Clustering Task.- Chapter 15. Dimensionality Reduction Task.- Appendices.

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