datawhalechina/math-for-ai

What it solves

Many people who want to learn machine learning or AI hit a wall because they lack the underlying math. This project brings together a complete, well-organized set of math topics — linear algebra, analytic geometry, matrix decomposition, vector calculus, probability and statistics, continuous optimization, and more — so learners can build the foundation they need before diving into algorithms.

How it works

The project is an open-source tutorial compiled by the Datawhale community. It translates and adapts the English book “Mathematics for Machine Learning” (the MML book) into Chinese, chapter by chapter. Each chapter has been translated and reviewed by volunteers. The content is presented as Markdown files that can be read online through a docsify-powered website, or locally by running docsify serve docs in the repository root. The table of contents lists 12 chapters, from an introduction to classification and support vector machines, with clear progress markers.

Who it’s for

The tutorial is aimed at:

• University engineering students (computer science, electrical engineering, automation, etc.) who need stronger math for AI courses.
• Beginners in machine learning and AI, including self-taught learners and working professionals.
• Practitioners who want to deepen their math understanding to move into advanced research or development.

Highlights

• Full Chinese translation of a well-known machine-learning math textbook.
• Covers a wide range of essential topics: linear algebra, geometry, matrix decomposition, calculus, probability, optimization, and more.
• Community-driven with active translation and proofreading volunteers.
• Free to read online and licensed under CC BY-NC-SA 4.0.
• Structured with chapter-level links and a progress checklist.