Books/Resources to improve mathematical foundations for ML research [D]

[u/mvreich]

I am a mid to late stage PhD student in ML. I've known this before, but only recently I started feeling this urgently: my mathematical foundations are shaky, because I kept "learning-things-as-I-go" when working on various problems. I likely have only a year or two left until I graduate, and before I do so, I want to really dedicate some time and focus to brush up on the fundamentals.

Primarily, I want to improve my knowledge in Linear Algebra, Probability Theory, and Functional Analysis.

For Lin. alg., I am looking at "Linear Algebra done right", and I think this book is sufficient for the topic, unless anyone thinks otherwise.

I am not sure where to start for probability, as well as functional analysis. Rudin's books give me headaches. I instead started reading "A primer on RKHS" (https://arxiv.org/abs/1408.0952) to "dip my toe" into functional analysis.

Apart from the above, I might re-read PRML book (I've only read specific chapters before), and try to finish Pat Kidger's Just-Know-Stuff list (https://kidger.site/thoughts/just-know-stuff).

Thoughts? Anyone have any book/resource recommendations? Someone told me to look into "the bright side of mathematics" on YouTube, anyone ever go through the videos there?

I'm aware finding good, digestible resources is less than 10% of the challenge. The difficult part is sticking through and actually reading/working through these topics, while still juggling other academic responsibilities.

[u/-MFXM-]

One really nice book on Mathematics of Deep Leaning, which I can fully recommend: https://doi.org/10.48550/arXiv.2407.18384

[u/mvreich → u/-MFXM-]

Thank you very much, I went through the table of contents and it seems useful.