This repository provides a comprehensive 200-page PDF study guide for Li Hang's "Statistical Learning Methods," aimed at students and practitioners seeking a deeper understanding of the subject. It offers detailed explanations of mathematical formulas and includes R code implementations for various algorithms, facilitating a hands-on learning experience.

How It Works

The project is a meticulously crafted study aid, presenting detailed derivations and explanations for the core concepts and algorithms covered in the foundational "Statistical Learning Methods" textbook. It bridges the gap between theoretical understanding and practical application by providing accompanying R code snippets for each major topic, enabling users to replicate and experiment with the methods.

Quick Start & Requirements

• Download the PDF: wget https://github.com/xlite-dev/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf
• Requires R for code examples.

Highlighted Details

• 200-page PDF detailing mathematical derivations.
• Covers 11 chapters of "Statistical Learning Methods."
• Includes R code implementations for algorithms like Perceptron, k-NN, Naive Bayes, Decision Trees, SVM, AdaBoost, EM, HMM, and CRF.
• Provides detailed explanations of concepts such as generalization error bounds, overfitting, and algorithm convergence.

Maintenance & Community

The project appears to be a personal effort with a single release (v0.1.0). The author indicates a current focus on LLM/VLM Inference and directs users to other repositories for related content.

Licensing & Compatibility

• License: GNU General Public License v3.0 (GPL-3.0).
• This license is copyleft, meaning derivative works must also be licensed under GPL-3.0. Commercial use or integration into closed-source projects may require careful consideration of licensing obligations.

Limitations & Caveats

The project is presented as a static PDF and accompanying R code, not an interactive tool or library. The author's focus has shifted, suggesting limited future development on this specific repository.