Statistical learning theory VS computational learning theory? What relations and differences are between statistical learning theory and computational learning theory?
Are they about the same topic? Solve the same problems, and use the same methods?
For example, the former says it is the theory of prediction (regression, classification,...).
 A: Computational learning, more concretely the probably approximately correct (PAC) framework, answers questions like: how many training examples are needed for a learner to learn with high probability a good hypothesis? how much computational effort do I need to learn with high probability such hypothesis? It does not deal with the concrete classifier you are working with. It is about what you can and cannot learn with some samples at hand.
In statistical learning theory you rather answer questions of the sort: how many training samples will the classifier misclassify before it has converged to a good hypothesis? i.e. how hard is it to train a classifier, and what warranties do I have on its performance? 
Regretfully I do not know a source where these two areas are described/compared in an unified manner. Still, though not much hope that helps
A: Supplementing the answer by @jpmuc, the distinction between computational and statistical learning seems to be a historical accident, and the theories are slowly merging (and sometimes being taught) as a single unified 'learning theory'. Computational and statistical learning theory are increasingly used as synonyms.
The main idea to come out of computational learning theory thus far is PAC learning, whose formulations often make use of the main contribution of statistical learning theory, the VC dimension.
For more detail and references: https://machinelearningmastery.com/introduction-to-computational-learning-theory/
