Zhi-Hua Zhou Bücher

Focusing on the core aspects of artificial intelligence, this textbook provides a thorough introduction to machine learning. It is structured into three parts: the first covers fundamental concepts and linear models; the second explores widely used methods such as decision trees and neural networks; and the third delves into advanced topics like feature selection and reinforcement learning. Each chapter features exercises and additional reading suggestions, encouraging readers to deepen their understanding of the subject.

Focusing on ensemble methods in machine learning, this introduction offers a comprehensive overview suitable for beginners. It covers essential algorithms like Boosting, Bagging, and Random Forest, alongside key concepts such as averaging, voting schemes, and diversity measures. Advanced topics include ensemble pruning and clustering, as well as recent developments in semi-supervised, active, cost-sensitive, and class-imbalance learning. The book aims to equip readers with both foundational knowledge and insights into cutting-edge techniques in the field.

Unsupervised learning.- Graph-based and semi-supervised learning.- Neural networks and deep learning.- Planning and optimization.- AI applications.