Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing. Many more applications are yet be defined. However, a better understanding of ML methods, their assumptions and guarantees would help software engineers adopt and identify the appropriate methods for their desired applications. We argue that this choice can be guided by the models one seeks to infer. In this technical briefing, we review and reflect on the applications of ML for software engineering organised according to the models they produce and the methods they use. We introduce the principles of ML, give an overview of some key methods, and present examples of areas of software engineering benefiting from ML. We also discuss the open challenges for reaching the full potential of ML for software engineering and how ML can benefit from software engineering methods. In particular, we will focus on three questions that we think should be addressed before attempting any new ML solution to an existing software engineering problem. These are: What class of learned models is appropriate for solving an SE problem? For this class of models, are there any existing learning algorithms that will work for typical instances and sizes of my SE problem? Otherwise, is it possible to adapt any fundamental ML principles to derive new learning algorithms? Has anyone considered a similar SE problem, and was it tractable to an ML solution?