In this lecture, I aim to provide an overview of the learning techniques that have found use in automated planning. Unlike most the clustering and classification tasks that have dominated the recent machine learning literature, learning in planning requires handling relational and first order representations, and foregrounds the need for knowledge-intensive learning techniques. I will start with a brief review of the planning models, and discuss the opportunities for learning in planning. I will then provide a survey of the explanation-based, case-based and inductive learning techniques that have been successfully used to tackle them.