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ML Resources This is a not-particularly-systematic attempt to curate a handful of my favorite resources for learning statistics and machine learning. This isn’t meant to be comprehensive, and in fact is still missing the vast majority of my favorite explainers. Rather, it’s just a smattering of resources I’ve found myself turning to multiple times and thus would like to have in one place. The organizatiion is as follows: •: Cover a fairly broad topic reasonably comprehensively, and would take weeks to months to work through start-to-finish. •: Explain a specific topic extremely clearly, and take minutes to hours (or a few days tops) to work through from start-to-finish. •: Provide structured access to useful bits of information on the order of seconds.

Finally, I’ve added a section with links to that often produce great content. Of the above, the second section is both the most incomplete and the one that I am most excited about. I hope to use it to capture the best explanations of tricky topics that I have read online, to make it easier to re-learn them later when I inevitably forget.

(In a perfect world, and/or would just write an article on everything, but in the meantime I have to gather scraps from everywhere else.) If you stumble upon this list and have suggestions for me to add (especially for the middle section!), please feel free to reach out! But I’m only trying to post things on here that I’ve read, so it may be caught in my to-read list for a while before it makes it on here. Of course, the source for this webpage is, so you can also just take it. Open Courses and Textbooks I’m trying to limit to this list to things that are legally accessible online, for free. Foundation File Description Math for machine learning book by Faisal and Ong, available on. Freely available book from Boyd and Vandenberghe on Applied LA (). Rachel Thomas has put together this great online textbook for computational linear algebra with accompanying.

John Tsitsiklis et al have put together some great resources. Their classic MIT intro to probability has been archived on and also offered on Edx (, ). The is also excellent. Joe Blitzstein’s undergrad probability course has a high overlap in content with 6.041.

Like 6.041, it also has a great, videos, and an offering. It’s a bit more playful, as well. This guy is amazing.

Some 250 youtube tutorials on ML, Probability, and Information Theory. What’s great about these playlists is any individual video could go into section 2!

Tim Roughgarden’s and Tim Roughgarden is one of most natural teachers I’ve ever seen, and fortunately for the world, he’s decided to make a lot of his algorithms resources public. The first link is to lecture notes in PDF form from many classes – for the data-oriented, his CS 168 course is accessible and amazing. Videos for his Algorithms 2 class (CS 261) are (pdf notes are in that first link). The second is a link to his page for his new textbook, but that page also has links out to all the youtube videos from his coursera version of CS 161 (Algorithms 1). Statistics File Description This is an online visual textbook that has a bunch of cool interactive displays for intro probability/stats ideas. My favorite is the inference visualizations. Russell Poldracks’ This appears to be a pretty fantastic (albeit rather elementary) textbook for a one-quarter intro to statistics class (stat 60 at stanford).