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:
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.