| 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 |