  There is a strong consensus that combining the versatility of machine
learning with the assurances given by formal verification is highly desirable.
It is much less clear what verified machine learning should mean exactly. We
consider this question from the (unexpected?) perspective of computable
analysis. This allows us to define the computational tasks underlying verified
ML in a model-agnostic way, and show that they are in principle computable.
