Machine learning is an important branch of computer science and artificial intelligence. As an introductory textbook in this field, this book covers all aspects of the basic knowledge of machine learning as much as possible. In order to enable as many readers as possible to understand machine learning through this book, the author tries to use mathematical knowledge as little as possible. However, a little knowledge of probability, statistics, algebra, optimization and logic seems inevitable. Therefore, this book is more suitable for undergraduate and graduate students of science and engineering above the third year of University, as well as people with similar backgrounds who are interested in machine learning. For the convenience of readers, the appendix of this book gives a brief introduction to some basic mathematical knowledge.
The book consists of 16 chapters, which are roughly divided into three parts: Part 1 (Chapters 1 to 3) introduces the basic knowledge of machine learning; Part 2 (Chapter 4 ~ 10) discusses some classical and commonly used machine learning methods (decision tree, neural network, support vector machine, Bayesian classifier, ensemble learning, clustering, dimension reduction and metric learning); Part 3 (chapters 11 ~ 16) is advanced knowledge, which involves feature selection and sparse learning, computational learning theory, semi supervised learning, probability graph model, rule learning and reinforcement learning. The subsequent chapters except the first three chapters are relatively independent, and readers can choose to use them according to their own interests and time. According to the class hours, the first 9 or 10 chapters can be taught for undergraduate courses in a semester; Graduate courses may wish to use the book.
In addition to Chapter 1, each chapter of the book gives ten exercises. Some exercises are designed to help readers consolidate the learning of this chapter, while others are designed to guide readers to expand relevant knowledge. These exercises can be used in the general course of one semester, supplemented by two or three large assignments for specific data sets. The exercises with asterisks are quite difficult, and some of them have no ready-made answers. They are just for the enterprising readers to think about.
This book can be used as a textbook for undergraduate or graduate students majoring in computer, automation and related majors in Colleges and universities, as well as for researchers and engineering technicians interested in machine learning.