📓 Awesome Free University AI/ML Course Notes
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, 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 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 |
| Harvard | CS181 – Machine Learning | — | github.com/harvard-ml-courses/cs181-textbook |
| Princeton | COS 324 – Introduction to Machine Learning | Sanjeev Arora, Danqi Chen | princeton-introml.github.io |
| Stanford | CS229 – Machine Learning | — | cs229.stanford.edu |
| UC Berkeley | CS189/289A – Introduction to Machine Learning | Jonathan Shewchuk et al. | eecs189.org |
| Caltech | CS156 – Learning From Data | Yaser Abu-Mostafa | work.caltech.edu/telecourse |
| Cornell | CS4780 – Machine Learning for Intelligent Systems | Kilian Weinberger et al. | cs.cornell.edu/courses/cs4780 |
🇬🇧 England
| University | Course | Instructor(s) | Notes |
|---|---|---|---|
| Oxford | Advanced Topics in Machine Learning (Bayesian ML section) | Tom Rainforth | cs.ox.ac.uk/teaching |
🇩🇪 Germany
| University | Course | Instructor(s) | Notes |
|---|---|---|---|
| LMU Munich | I2ML – Introduction to Machine Learning | SLDS group | slds-lmu.github.io/i2ml |
🇸🇬 Singapore
| University | Course | Instructor(s) | Notes |
|---|---|---|---|
| National University of Singapore (NUS) | Joint Lecture Notes (CS4248 Introduction to LLMs, CS5246 Text Mining, CS5228 Data Mining) | Christian von der Weth | https://github.com/chrisvdweth/selene |
🇰🇷 South Korea
| University | Course | Instructor(s) | Notes |
|---|---|---|---|
| KAIST | Machine Learning (iNotes series) | iAI Lab | 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 |
🌍 Universities checked, no qualifying entry (yet)
So the list is transparent about its own limits — and so contributors don't re-suggest something already ruled out — here's what was checked and didn't qualify, mostly because material is slides/video-only or sits behind a login:
Yale, University of Washington, Carnegie Mellon University, University of Cambridge, TU Munich, Karlsruhe Institute of Technology, École Polytechnique, Sorbonne Université, Universitat Politècnica de Catalunya, Universidad Politécnica de Madrid, Universidad Autónoma de Madrid, Instituto Superior Técnico (Lisbon), Universidade do Porto, Universidade de Aveiro, Lomonosov Moscow State University, HSE University, ITMO University, Tsinghua University, Peking University, Zhejiang University, University of Tokyo, Kyoto University, Institute of Science Tokyo, IIT Bombay, IIT Delhi, IISc Bangalore, UNICAMP, ITA, UFG.
If you know of a genuinely qualifying, official, written set of notes at any of these (or elsewhere), please open an issue — see CONTRIBUTING.md.
🤝 How to Contribute
Read CONTRIBUTING.md before opening a PR. Quick version:
- The material must be written notes, official, free, and permanent (no login).
- Link to the primary source (the professor's or course's own page).
- One entry per PR, alphabetical by country, then by university.
⚖️ Why Official Links Only
Same philosophy as the parent project: this repository doesn't host any files. It's an index pointing to what universities and instructors already publish openly. If a link breaks, please open an issue.
Made with 📓 for students everywhere.
Curated by Marcos Cruz — feel free to connect on LinkedIn.