Author: Zhi-Hua Zhou Full Title: Machine Learning Publisher: Springer; 1st ed. 2021 edition (August 21, 2021) Year: 2021 ISBN-13: 9789811519673 (978-981-15-1967-3), 9789811519666 (978-981-15-1966-6) ISBN-10: 9811519676, 9811519668 Pages: 459 Language: English Genre: Educational: Machine Learning File type: EPUB (True), PDF (True) Quality: 10/10 Price: 62.39 € Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning. Overview: ✓ Provides a comprehensive and unbiased introduction to almost all aspects of machine learning ✓ Received a Chinese literature prize for its elegant presentation ✓ The Chinese version has sold 200,000+ copies ------------- Our members see more. Join us!
