Machine Learning Author: Zhi-Hua Zhou

Machine Learning by Zhi-Hua Zhou, translated by Shaowu Liu, is a definitive textbook that balances rigorous theory with real-world relevance. Published by Springer, this work presents a structured introduction to the core principles and key methodologies behind modern machine learning systems. With clarity and precision, Zhou navigates through topics like supervised learning, unsupervised learning, reinforcement learning, and ensemble methods—backed by mathematical formulation and intuitive explanation.

Designed for both undergraduate and graduate-level students as well as professionals, the book offers:

A conceptual foundation in pattern recognition and statistical modeling

In-depth analysis of algorithms such as decision trees, neural networks, SVMs, and boosting

Practical applications in fields ranging from computer vision to data mining

Exercises and illustrations that reinforce learning and bridge gaps in understanding

Critical discussion on the limitations, ethical implications, and future directions of AI

Zhi-Hua Zhou’s authoritative style and Shaowu Liu’s faithful translation make this edition widely accessible to non-native English speakers. It has been embraced globally for its comprehensive coverage and pedagogical value, becoming a staple in academic and research institutions.