[LolWolf]

Take a peek at our class [0]! We present a bunch of topics not covered in undergraduate courses (at least to our knowledge), such as proximal gradient descent, random features, etc., without referencing any probability. The course is aimed at mostly Sophomores/Juniors in any STEM field with the only prerequisite being the introductory linear algebra course EE103. [1] All of the material (minus solutions) is available online for both courses.

I'm probably highly biased, but I'd like to say this is a fairly fresh take which departs heavily from the usual CS229 (Ng's course) presentation style and order since it's meant for a completely different audience (and was, to be fair, written this past quarter, unlike 229 which was written perhaps 15 years ago).

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[0] http://ee104.stanford.edu

[1] http://ee103.stanford.edu

[stochastic_monk]

I used Stanford’s materials for CS229 as a reference in two of the three graduate ML classes I’ve taken. I’ve been quite impressed, and I don’t doubt ee103 is also quite well done.

[wodenokoto]

> All of the material (minus solutions) is available online for both courses.

I can only find lecture slide when looking at the course site.

Are there no readings or problems?

[LolWolf]

There are problems, but I’m afraid that we took them down since we need to rewrite a few before next year. Sorry! I’m sure poking around the site might yield some results, if you’re careful enough ;) (at least for 104, someone else is teaching 103 and I’m not sure what all they’re doing with the course, but all of the main problems are available on the book’s website).