WenDesi/lihang_book_algorithm resource

Health v2 · maintenance only

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5827 stars · 1936 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python repository implementing the algorithms from Dr. Li Hang's textbook 'Statistical Learning Methods' chapter by chapter, including perceptron, KNN, naive Bayes, decision trees, logistic regression, maximum entropy, SVM, AdaBoost, and HMM. Each implementation is paired with a blog post explaining the approach, mostly demonstrated on the MNIST dataset.

Use cases

• learn machine learning algorithms by reading clean Python implementations
• study algorithms from the Statistical Learning Methods textbook with code
• see how perceptron, KNN, naive Bayes, and SVM work on MNIST
• find reference implementations of AdaBoost and hidden Markov models
• supplement textbook study with hands-on coding examples

When to choose

• you are studying Li Hang's Statistical Learning Methods and want code to follow along
• you want simple, readable from-scratch implementations of classic ML algorithms
• you are a student or beginner learning how these algorithms work internally

When to avoid

• you need production-ready, optimized, or well-tested ML libraries
• you need algorithms beyond the book's coverage or modern deep learning
• you require a maintained project with a license and active support

Facets

learning-resource · maturity maintenance

machine-learning machine-learning tutorials education python statistical-learning-methods lihang mnist algorithm-implementations educational

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