Complete, stand alone Stanford machine learning course notes

Here are my notes (on github). Nowhere near as polished as this version, and probably reveals more about my process of understanding than machine learning itself, but if we're sharing: <a href="https://github.com/mechamoth/ml-class/blob/master/ML_Notes.org" rel="nofollow">https://github.com/mechamoth/ml-class/blob/master/ML_Notes.o...</a>

Thanks for the great resource. I wish there was a way every student could share notes with everyone.

Just something that caught my eye: In the introduction, you have written "clarification problem", which of course should be "classification problem".

Thanks for this, especially for annotating the lectures. It's much easier to skim/review this way. I was rewatching the computer vision pipeline lectures over the weekend when it occurred to me that they might not be available when the new class starts.

Thank you good sir, wish more people shared their quality notes like you!

Wow! What a great resource, thanks!

What math preliminaries are necessary to understand machine learning? I'm taking a basic linear algebra and probability course in the Spring...I'm wondering if that would be enough.

Why not a git repo on Github? So people can contribute.