This repository serves as a study companion for the Chinese textbook ‘Introduction to Machine Learning Theory’ authored by Zhou Zhihua and colleagues. It supplements the original text with detailed proofs, concept explanations, and case studies to help readers navigate the theoretical aspects of machine learning. The material covers seven core theoretical concepts: learnability, complexity, generalization bounds, stability, consistency, convergence rates, and regret bounds. The content is maintained as Jupyter Notebooks, making it interactive and accessible for learners.