[account2]

Thanks for the context. That sounds like a good plan to me regarding post-doc locations. I am also interested in theory side of ML. What areas of mathematics should one learn really well that apply to the theory side? What blogs, papers, books would you point one to to learn the theory side more? To your knowledge are their applications of abstract algebra to ML? If so, what areas of algebra apply & what problems do they solve?

[PAClearner]

I could rant about this for hours. I actually just went to a defense for a deep learning paper that had a ton of abstract algebra. I am honestly not really a fan of deep learning and algebra because all the papers to me like- seem to stop at describing some really basic feedforward network as some really specific mathematical structure but these theories a) provide very little explanation of empirical phenomena b) provide no new directions of research in terms of like useful network architectures.

I haven't really come across algebra in machine learning other than people applying it to deep learning.

i.e. https://arxiv.org/pdf/1802.03690.pdf

ie. https://icml.cc/Conferences/2018/Schedule?showEvent=2048

I don't personally find papers like this valuable but idk I have never really enjoyed abstract algebra.

For areas of mathematics to do theory in ML (and to do ML more generally!)

-probability/concentration/hoeffding bounds [the PAC model] [Key]

-linear algebra [key]

-optimization [key]

for books

-understanding machine learning by shai ben david

This book is nice since it really balances theory with a more practical understanding.

-An Introduction to Computational Learning Theory by kearns is a classic [low priority]. this is fun since the proofs are simple and deep but is very very far away from practical algorithms.

-convex optimization by boyd

Course Notes:

[I think a good alternative to blogs is stalking course notes for other schools-they are very often public.]

- http://ttic.uchicago.edu/~avrim/MLT18/index.html

good learning theory course by avrim blum who is a big deal in learning theory and theory.

- tim roughgardens notes are a blessing for algorithms and theory [seriously he should have a patreon or something]

https://theory.stanford.edu/~tim/notes.html

Blogs:

-http://www.argmin.net/

this is ben recht's blog and is filled with ML wisdom.

-https://blogs.princeton.edu/imabandit/ not quite learning theory but a lot of ML adjacent stuff

I don't read many blogs as I should tbh so other people can give better advice

VIDEOS https://www.youtube.com/channel/UCW1C2xOfXsIzPgjXyuhkw9g

This is the simons institute youtube channel. probably the best single location for recordings of TALKS in computer science-good amount of ML talks.

https://simons.berkeley.edu/videos