[CuriousCosmic]

Yeah machine learning is more or less just very complex application of control theory techniques and notably it is usually done by people without formal control theory backgrounds.

Super useful for control applications but obviously you really want to know control theory so that you aren't just using ML to throw darts at a wall.

[SoftTalker]

> using ML to throw darts at a wall

A bit off topic but I had a laugh at that, reminded me of that ridiculous Meta commercial where the girl at a pool table asks Meta which ball should she hit?

[rcxdude]

More like optimization. Signal processing and control theory are basically the same maths with different applications, optimization is a bit different but has some overlap (especially with, e.g. optimal control techniques).

[CuriousCosmic]

Eh to a certain degree. The architecture of the models is very much control theory and then the training is control system tuning (which of course is an optimisation problem like you said).

I would definitely agree that optimisation fits the definition in part but I find really only control theory covers that entire field of signal processing, optimisation, and decision making systems.

And importantly, because ML in some amount touches on all of those, control theory tends to fit better as it focuses so heavily on providing a comprehensive framework for reasoning about all of those elements together.