You should keep in mind that the division between a traditional frequentist approach with focus on predictions and correlations only and a Bayesian approach with an emphasis on estimations and causations, is not that sharp. Machine learning can be frequentist with ensemble methods (EMB) as examples and Bayesian with Gaussian Processes as examples.
If one views ML from a statistical learning perspective, one is then equally interested in estimating errors as one is in finding correlations and making predictions. It is important to keep in mind that the frequentist and Bayesian approaches differ mainly in their interpretations of probability. In the frequentist world, we can only assign probabilities to repeated random phenomena. From the observations of these phenomena, we can infer the probability of occurrence of a specific event. In Bayesian statistics, we assign probabilities to specific events and the probability represents the measure of belief/confidence for that event. The belief can be updated in the light of new evidence.