Kinda-sorta. ML is a bit more about optimisation theory, not control, but there's a lot of overlap: you can consider controllers to be optimisers, and optimiser

Kinda-sorta. ML is a bit more about optimisation theory, not control, but there's a lot of overlap: you can consider controllers to be optimisers, and optimisers can be used in control (a sub-field called optimal control). One big constraint in control is it has to be causal, i.e. it only sees part of the function it is trying to optimise (the part that is in the past) and only has the ability to influence part of the input in the system it is optimising (the part in the future). Control also has a huge amount of overlap with signal processing, both analog and digital (most of the math is basically the same).