Machine Learning

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What kind of mathematical foundations are required for conducting research across the vast specialised branches of AI/ML/DL? [D] (alien.top)

submitted 1 year ago by HopeIsGold@alien.top to c/machinelearning@academy.garden

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The absolute basic mathematics that is required to understand basic ML/DL are calculus, linear algebra, probability and some convex optimisation. We are all aware of that.

But ML and DL has become a vast field both in breadth and depth. A single person can't understand the field entirely. There are specialistions and sub-specialisations and further more.

If you work in a branch of ML/DL research where some other math fundamentals are needed to understand research papers and do innovative research, can you mention your field of work and the math fundamentals that are required to gain entry into your field?

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[–] todeedee@alien.top 1 points 1 year ago

A little bit of functional analysis wouldn't hurt. Knowing your fourier / laplace operators could help clarify some of the logic underlying CNNs / Neural ODEs.

Group theory seems to be ramping up with invariance / equivariant properties of some specialized neural networks (i.e. rotation equivariant nets, gauge equivariant nets).

Causal inference is another emerging hot topic - but it is a bit scatterbrained, since there are multiple competing schools of thought surround this topic.