Principles of Machine Learning

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.

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.