Lihang
Lihang is a comprehensive educational repository dedicated to Li Hangs Statistical Learning Methods, covering both the first and second editions of the book. It provides detailed study notes, executable Python code, Jupyter notebooks, and a collection of references to facilitate deep learning of statistical algorithms. The repository has been updated to align with the 2019 second edition, adding eight unsupervised learning methods to complete the discussion of major data mining algorithms. Key features include a custom glossary and symbol index to clarify notation, an unofficial errata list for error tracking, and a bash script to automatically download bibliographic references mentioned in the text. The content systematically explains the three core elements of statistical learning models, strategies, and algorithms across various topics such as Perceptron, k-Nearest Neighbors, Naive Bayes, Support Vector Machines, Decision Trees, Hidden Markov Models, Conditional Random Fields, and Probabilistic Latent Sema