Machine Learning

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 basic machine learning knowledge as much as possible in terms of content. The book has 16 chapters in total, which is roughly divided into 3 parts: Part 1 (Chapter 1~3) introduces the basic knowledge of machine learning; Part 2 (Chapter 4-10) discusses some classic and commonly used machine learning methods (decision tree, neural network, support vector machine, Bayesian classifier, ensemble learning, clustering, dimensionality reduction and metric learning); Part 3 (Chapters 11 to 16) is advanced knowledge, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, reinforcement learning, etc. Each chapter is accompanied by exercises and introduces relevant reading materials to facilitate further exploration by interested readers. This book can be used as a textbook for undergraduates or graduate students in computer, automation and related majors in colleges and universities, and can also be used as a reading reference for researchers and engineering technicians who are interested in machine learning.