The activity is aimed at the presentation and experimentation of specific techniques of machine learning applied to the world of networks. The activity consists of two parts: a first theoretical part and a second more practical one.The first part will discuss both the main techniques for collecting data and measurements from computer networks, and the main machine learning techniques with which such data can be analyzed and interpreted. In particular, the following topics will be presented:- Active and passive network measurement methods - Network data visualization techniques -Machine learning techniques for data classification -Applications of classification techniques to network data: traffic classification and anomaly detection.

In the second part, the students will have to apply the previously illustrated techniques to a data set of cellular network measurements: the objective is to classify the cellular network users as satisfied or unsatisfied with the network service. Students organized in "teams", will have to adapt appropriately the machine learning algorithms studied during the first part, train them using the training data set and apply them to the validation data sets. The teams with the best accuracy in the classification of users will present their results to other participants.

dal March 2019 a April 2019