fengdu78/lihang-code

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19606 stars · 6224 forks observed · 2026-10-14

What it is AI-extracted, prompt v1, taxonomy v1, 2026-10-19, confidence 0.97

A collection of Jupyter Notebook implementations of the algorithms described in Li Hang's "Statistical Learning Methods" textbook. The repository provides reference code for a wide range of supervised and unsupervised learning techniques.

Use cases

• implement perceptron algorithm from scratch
• run k-nearest neighbors examples for a class project
• study naive Bayes classification with real code
• experiment with decision tree learning in Python notebooks
• learn support vector machine implementation step‑by‑step
• explore EM algorithm and hidden Markov models for coursework
• apply clustering and PCA techniques using the provided notebooks

When to choose

• You need reference implementations of classic statistical learning algorithms for learning or teaching purposes
• You want Jupyter notebooks that walk through the math and code together
• You are preparing for interviews that reference Li Hang's textbook
• You need a self‑contained, language‑agnostic set of examples in Python

When to avoid

• You require a production‑grade, optimized ML library
• You need GPU‑accelerated or deep‑learning frameworks
• You prefer a package with a stable pip installable API

Facets

library · maturity active

machine-learning data-science artificial-intelligence machine-learning data-science python statistical-learning lihang textbook-implementation ml-algorithms jupyter-notebook

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