wzyonggege/statistical-learning-method resource

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2180 stars · 662 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks implementing the algorithms from the classic Chinese textbook 'Statistical Learning Methods' (统计学习方法) in Python, covering least squares, perceptron, kNN, Naive Bayes, decision trees, logistic regression, SVM, AdaBoost, and EM. It is educational material first, with hand-written implementations and scikit-learn used only for comparison.

Use cases

• learn statistical learning algorithms from scratch in python
• study the statistical learning methods textbook with code
• implement perceptron knn svm adaboost by hand
• notebooks explaining the math behind classic ml algorithms
• practice machine learning fundamentals with jupyter
• compare hand-written implementations with scikit-learn

When to choose

• you are studying Li Hang's Statistical Learning Methods and want runnable code per chapter
• you want to understand classic ML algorithms by reading from-scratch implementations
• you prefer notebook-based, math-plus-code learning material

When to avoid

• you need a production machine learning library
• you want modern deep learning or transformer-based methods
• you need actively developed features rather than maintained educational notes

Facets

learning-resource · maturity maintenance

machine-learning data-science developer-tools machine-learning education tutorials data-science python cross-platform jupyter-notebooks statistical-learning chinese textbook-companion algorithms-from-scratch scikit-learn

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