Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated collection of machine learning and AI lecture notes from the world's leading universities. This repository gives you access to the same lecture notes used by students at top institutions such as MIT, helping you learn from the very best educational resources available.
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# 📓 Awesome Free University AI/ML Course Notes [](https://awesome.re) [](CONTRIBUTING.md) [](https://github.com/MarcosSete/awesome-free-ai-course-notes/actions/workflows/link-check.yml) [](LICENSE) > A curated list of **official, free, written course notes** on AI/Machine Learning from top universities worldwide — the kind some departments publish instead of assigning a paid textbook. This is a companion project to [Awesome Free AI Books](https://github.com/MarcosSete/awesome-free-ai-books), focused on a different (and much rarer) kind of resource: course notes written and published directly by the instructors themselves, freely available, and detailed enough to function as a textbook substitute. ## What counts as an entry here This list is intentionally strict. An entry must be: - **Written prose notes** — not slide decks, not video-only lectures. If it reads like a textbook chapter, it qualifies. If it's a slide deck or a recorded lecture with no accompanying text, it doesn't (see [CONTRIBUTING.md](CONTRIBUTING.md) for the full reasoning and the discussion that shaped this rule). - **Official** — published by the professor, the course, or the department itself. - **Free and permanent** — no login, no institutional email required, no paywall. Because of this strict bar, most universities — even excellent ones — simply don't have a qualifying entry: they use a textbook, or their materials are slides/video, or everything sits behind a Moodle/Canvas login. That's expected and it's why this list is short. Quality and honesty over quantity. --- ## 🇺🇸 United States | University | Course | Instructor(s) | Notes | |---|---|---|---| | MIT | 6.390 – Introduction to Machine Learning | EECS Dept. | [introml.mit.edu/notes](https://introml.mit.edu/notes/) | | Harvard | CS181 – Machine Learning | — | [github.com/harvard-ml-courses/cs181-textbook](https://github.com/harvard-ml-courses/cs181-textbook) | | Princeton | COS 324 – Introduction to Machine Learning | Sanjeev Arora, Danqi Chen | [princeton-introml.github.io](https://princeton-introml.github.io/) | | Stanford | CS229 – Machine Learning | — | [cs229.stanford.edu](https://cs229.stanford.edu/) | | UC Berkeley | CS189/289A – Introduction to Machine Learning | Jonathan Shewchuk et al. | [eecs189.org](https://eecs189.org/) | | Caltech | CS156 – Learning From Data | Yaser Abu-Mostafa | [work.caltech.edu/telecourse](https://work.caltech.edu/telecourse) | | Cornell | CS4780 – Machine Learning for Intelligent Systems | Kilian Weinberger et al. | [cs.cornell.edu/courses/cs4780](https://www.cs.cornell.edu/courses/cs4780/2018fa/) | ## 🇬🇧 England | University | Course | Instructor(s) | Notes | |---|---|---|---| | Oxford | Advanced Topics in Machine Learning (Bayesian ML section) | Tom Rainforth | [cs.ox.ac.uk/teaching](https://www.cs.ox.ac.uk/teaching/courses/2019-2020/advml/) | ## 🇩🇪 Germany | University | Course | Instructor(s) | Notes | |---|---|---|---| | LMU Munich | I2ML – Introduction to Machine Learning | SLDS group | [slds-lmu.github.io/i2ml](https://slds-lmu.github.io/i2ml/) | ## 🇰🇷 South Korea | University | Course | Instructor(s) | Notes | |---|---|---|---| | KAIST | Machine Learning (iNotes series) | iAI Lab | [iailab.kaist.ac.kr/teaching/machine-learning](https://iailab.kaist.ac.kr/teaching/machine-learning) | ## 🇧🇷 Brazil | University | Course | Instructor(s) | Notes | |---|---|---|---| | USP (Escola Politécnica) | Introdução ao Aprendizado de Máquina | Hae Yong Kim | [lps.usp.br/hae/apostila](http://www.lps.usp.br/hae/apostila/introaprend-ead.pdf) | --- ## 🌍 Universities checked, no qualifying entry (yet) So the list is transparent about its own
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c9237df46349dfc2, readme:curated list, name:course, readme:course