> That is, signal processing had Nyquist's rates. And typically knows there is an underlying signal. Does ml have either?

What does this question mean? Every band-limited signal has a Nyquist rate. Most signals of interest are well-contained within some finite bandwidth (e.g., human voice). Sampling above this rate will get you very little.

If you're building an ML model to process a certain class of sampled signal and you know, for example, 99% of the signal energy falls within a certain frequency range, that should guide your choice of sample rate. If you're sampling at too high a rate, your input layers may have far more parameters than are needed or useful.

Whether or not a given ML input actually contains a signal of interest doesn't seem relevant to how you sample and preprocess the signal.