This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, learn to get what, and how to evaluate, as well as what does it mean 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 below: introduction (Chapter 1), evaluation (Chapter 2), supervised learning (Chapter 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 and so on. It can also be used as a reference for readers who are interested in machine learning and hope to make contributions to the field.