Introduction to Machine Learning – Overview of the Tsinghua University Textbook

The book "Introduction to Machine Learning" authored by Professor Wang Dong of Tsinghua University Speech and Language Center, published by Tsinghua University Press, includes a foreword by Professor Zhu Xiaoyan.

The main goal of the book is not to discuss each algorithm in detail but to organize seemingly advanced methods, explain their basic ideas, usage, and relationships with other techniques, helping students enter the grand realm of machine learning.

In addition to the main author, teachers Feng Yang, Wang Caixia, and Wang Maoning contribute chapters on graphical models, kernel methods, and genetic algorithms respectively, providing students with solid foundations for deeper research.

The book is divided into eleven chapters covering: 1. Overview of machine‑learning research, history, and key issues. 2. Linear models, including prediction, classification, and Gaussian models. 3. Fundamentals, structures, and training of neural networks. 4. Basics and recent advances of deep neural networks. 5. Kernel methods, especially support‑vector machines. 6. Graph models and related learning/inference methods. 7. Unsupervised learning, clustering, and manifold learning. 8. Non‑parametric Bayesian models, focusing on Gaussian and Dirichlet processes. 9. Evolutionary learning such as genetic algorithms and programming. 10. Reinforcement learning, including deep reinforcement learning. 11. Various numerical optimization methods.

Readers can obtain the complete textbook by scanning the QR code or replying “机器学习导论” ("Introduction to Machine Learning") to the linked public account.