It may be that technical prereqs are missing. It could also be that you’re missing a broader sense of “mathematical maturity”, or that you’re struggling because Stuart’s work is simply hard to understand. That said, useful prereq areas (in which you could also gain overall mathematical maturity) would include:

Probability theory

Linear Algebra

Machine learning theory

Reinforcement Learning

It’s probably overkill to go deep into these topics. Usually, what you need is in the first chapter.

If this is true, then this post by Michael Nielsen may be interesting to the poster. He uses a novel method of understanding a paper by using Anki to learn the areas of the field relevant to, in this case, the AlphaGo paper. I don’t have a good reason to do this right now, but this is the strategy I would use if I wanted to understand Stuart’s research program.

Yep, I’ve seen that post before. I’ve tried to use Anki a couple times, but I always get frustrated trying to decide how to make things into cards. I haven’t totally given up on the idea, though, I may try it again at some point, maybe even for this. Thanks for your comment.

Also, NB, your link is not formatted properly—you have the page URL, but then also “by Michael Nielsen is interesting” as part of the link, so it doesn’t go where you want it to.

Thanks, this is helpful! Mathematical maturity is a likely candidate—I’ve done a few college math courses (Calc III, Linear Alg, Alg I), so I’ve done some proofs, but probably nowhere near enough, and it’s been a few years. Aside from Linear Alg, all I know about the other three areas is what one picks up simply by hanging around LW for a while. Any personal recommendations for beginner textbooks in these areas? Nbd if not, I do know about the standard places to look (Luke Muehlhauser’s textbook thread, MIRI research guide, etc), so I can just go look there.

You could check out Best Textbooks on Every Subject. But people usually recommend Linear Algebra Done Right for LinAlg. Understanding ML seems good for ML Theory. Sutton and Barto is an easy read for RL.

It may be that technical prereqs are missing. It could also be that you’re missing a broader sense of “mathematical maturity”, or that you’re struggling because Stuart’s work is simply hard to understand. That said, useful prereq areas (in which you could also gain overall mathematical maturity) would include:

Probability theory

Linear Algebra

Machine learning theory

Reinforcement Learning

It’s probably overkill to go deep into these topics. Usually, what you need is in the first chapter.

If this is true, then this post by Michael Nielsen may be interesting to the poster. He uses a novel method of understanding a paper by using Anki to learn the areas of the field relevant to, in this case, the AlphaGo paper. I don’t have a good reason to do this right now, but this is the strategy I would use if I wanted to understand Stuart’s research program.

Yep, I’ve seen that post before. I’ve tried to use Anki a couple times, but I always get frustrated trying to decide how to make things into cards. I haven’t totally given up on the idea, though, I may try it again at some point, maybe even for this. Thanks for your comment.

Also, NB, your link is not formatted properly—you have the page URL, but then also “by Michael Nielsen is interesting” as part of the link, so it doesn’t go where you want it to.

There appears to be some sort of bug with the editor, I had to switch to markdown mode to fix the comment. Thanks for the heads up.

I use Anki for this purpose and it works well as long as you already have a system to give you a strong daily Anki review habit.

Thanks, this is helpful! Mathematical maturity is a likely candidate—I’ve done a few college math courses (Calc III, Linear Alg, Alg I), so I’ve done some proofs, but probably nowhere near enough, and it’s been a few years. Aside from Linear Alg, all I know about the other three areas is what one picks up simply by hanging around LW for a while. Any personal recommendations for beginner textbooks in these areas? Nbd if not, I do know about the standard places to look (Luke Muehlhauser’s textbook thread, MIRI research guide, etc), so I can just go look there.

Nate Soares wrote a series of textbook reviews, and I have as well—there’s discussion of why books were selected and what we got from them.

You could check out Best Textbooks on Every Subject. But people usually recommend Linear Algebra Done Right for LinAlg. Understanding ML seems good for ML Theory. Sutton and Barto is an easy read for RL.

Thanks!