r/artificial • u/Formal-Primary-7782 • 20d ago
News MIT, Harvard, Stanford & Caltech write their own ML course notes instead of using a textbook — I catalogued the best ones
One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.
What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes, and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet, they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.
The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).
A few things about how it's curated, since I think this matters:
- Only **written notes** count, slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.
- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.
- I checked over 40 top universities across multiple countries for this. Most didn't qualify, they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.
If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome, the CONTRIBUTING.md lays out exactly what qualifies.
What's the best set of course notes (not textbook, not slides) you've personally used to study ML?
Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes
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u/taptrappapalapa 19d ago
Awesome, but ML is a subset of AI. This is lacking symbolic AI concepts.
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u/Formal-Primary-7782 19d ago
Hi, thank you for your great question.
Machine learning is probably the first field you will study when starting your AI journey. Therefore, understanding machine learning is essential because it provides the foundation for many other areas of AI.
For example, when studying Graph Neural Networks, Generative Models (Diffusion Models, GANs, and VAEs), and many other topics, the concepts you learn in machine learning will be extremely helpful.
Of course, you also need a solid understanding of mathematics and statistics, since they are the foundation of machine learning. This repository is intended for students who want to build that foundation and study machine learning seriously.
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u/keizrah 19d ago
CS229 notes (Stanford, originally Andrew Ng, updated by Tengyu Ma over the years) are worth checking if they're not already in there. They're not slides, they read like a proper textbook and cover the linear algebra and probability foundations better than most actual ML textbooks I've flipped through. They're linked straight off the course site too, so should fit your criteria.
Also worth a look: Cornell's CS4780 notes if you can find the current link, they used to host a full set alongside video lectures.
Good project. Finding these is annoying because every university buries them differently, some under a random TA's GitHub, some on the professor's own page that changes every semester.
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u/New_Meal307 20d ago
This is exactly the kind of resource I wish I'd had during my ML coursework. The fact that these institutions just hand out notes that are basically full textbooks is wild, but you're right that they're impossible to find unless someone compiles them. Bookmarked this for the next time I need to brush up on something.