Title: "Learning Under Concept Drift: Methodologies and Applications"

Most machine learning techniques assume that the process generating the data is stationary. This guarantees that the model learned during the initial training phase remains valid during the subsequent operation. Unfortunately, stationarity is often an oversimplifying assumption because real-world processes typically change overtime. In the classification literature, this problem is referred to as concept drift.

Learning under concept-drift is a challenging research topic. In fact, on top of the usual online learning issues, the learner has to deal with eventual changes in the data-generating process that would make obsolete the designed application. Given the fact that changes are often unpredictable, as they might occur at any time and shift the data-generating process to an unforeseen state, the learner has to evolve. This can be achieved either by undergoing a continuous application update to match the recent operating conditions (passive approach) or steadily monitoring the data stream to detect changes and, eventually, react (active approaches). In the last few years, there has been a flourishing of solutions for learning under concept drift, also given the large number of applications where these techniques can be employed.

The tutorial introduces the main issues of learning under concept drift, the active and passive approaches as two extreme adaptation strategies, and few relevant applications such as those related to fraud-detection or those meant for detecting anomalies/changes in streams of signals and images.