r/learnmachinelearning Feb 14 '26

Help Statistical Learning Or Machine Learning first?

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ISLP book, I finished the first 2 chapters, but this book is not easy, and I want some guys to study this book together. Any tips to study this book?

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u/Radiant-Rain2636 Feb 14 '26

Somebody compiled this and It’s good.

https://www.reddit.com/r/GetStudying/s/9fnpxdzMGM

Pick your courses and resources from here

u/zx7 Feb 14 '26
  • Some of those topics can be cut if you want to focus on Machine Learning. E.g. Number Theory, Complex Analysis, Category Theory.
  • You really just need up to ODEs and Probability and Statistics.
  • I'm sure Differential Geometry has its place in Machine/Deep Learning, but I've not encountered a scenario where it is absolutely necessary.
  • PDEs, Measure Theory and Functional Analysis have some applications if you want to study the theory behind StableDiffusion.
  • Fourier Analysis (not listed) would be far more important for audio and probably vision as well. A good series of books on Analysis is by Elias Stein (Fourier, Real, Complex), the PhD advisor of Terence Tao. I'd recommend Fourier Analysis after Linear Algebra. It really reveals a completely new way of thinking about functions. It's basically a prerequisite for Functional Analysis.
  • You don't really need much Graph Theory other than the very basics (except for Graph Neural Networks) as far as I'm aware. Far more important is algorithms on graphs (depth first search, breadth first search, etc.).

u/Healthy-Educator-267 Feb 14 '26

Measure theory and functional analysis are the bedrock of probability theory so it’s broadly applicable (or lurking behind the scenes) even outside of diffusion theory

u/zx7 Feb 14 '26

Sure, something like Gaussian processes would require a more abstract notion of probability measure. But for most ML applications, you can get away without knowing the formal definition of a measure or any functional analysis.

u/Healthy-Educator-267 Feb 14 '26

Most applied ML work in industry requires basically no math at all since modeling is almost commoditized now. Engineering skills (very broadly construed) dominate any academic ones.

But yeah formally any continuous time process requires understanding the formal notion of a conditional expectation at minimum and usually much more, so yeah measure theory becomes unavoidable there. As for functional analysis, it’s again lurking in the background since statistical learning theory and nonparametrics are about estimating / optimizing in infinite dimensional spaces of functions. I think it shows up more explicitly when discussing kernel methods since RKHS is where the action is. Again, with continuous time stochastic processes (such as Gaussian processes) you are dealing with probability on Banach spaces.