math
🧮 Path to a free self-taught education in Mathematics!
A free, self-paced curriculum covering a full undergraduate mathematics degree using online courses from MIT, Harvard, and Stanford, organized into a structured two-year learning path.
This repository is a free, self-taught university-level mathematics curriculum put together by the Open Source Society University (OSSU). It is not software. It is a structured list of courses that, taken together, cover the material of an undergraduate mathematics degree using freely available online resources from universities like MIT, Harvard, and Stanford.
The curriculum has two main sections. Core Mathematics contains the required courses that all students are expected to complete: mathematical thinking, calculus, differential equations, discrete mathematics, linear algebra, probability and statistics, analysis, and abstract algebra. Advanced Topics contains elective areas where students pick one course from each category and then go deeper into one area of their choosing.
The estimated time to complete the full curriculum is about two years if you study roughly 18 to 22 hours per week. Most of the courses are free to access, though some charge for graded assignments or certificates. You can work through the material alone or with others, in order or out of order, though the courses are arranged to follow their prerequisites.
There is a Discord community for students to connect, ask questions, and support each other. You can also raise issues or suggest curriculum changes through GitHub.
The project is aimed at people who want a serious grounding in mathematics for its own sake, or as a foundation for other technical fields. It is designed for self-motivated learners who are comfortable studying independently with community support rather than through a formal institution.
Where it fits
- Study calculus, linear algebra, and abstract algebra through free university courses without enrolling in a degree program
- Follow a structured two-year curriculum to build a math foundation for machine learning or computer science
- Pick elective advanced topics like probability or analysis to fill specific gaps in your existing math knowledge