Traditionally the field of machine learning has had its main focus on predictions and correlations. These concepts outline in some sense the difference between machine learning and what is normally called Bayesian statistics or Bayesian inference.
In machine learning and prediction based tasks, we are often interested in developing algorithms that are capable of learning patterns from given data in an automated fashion, and then using these learned patterns to make predictions or assessments of newly given data. In many cases, our primary concern is the quality of the predictions or assessments, and we are less concerned with the underlying patterns that were learned in order to make these predictions. This leads to what normally has been labeled as a frequentist approach.