Information Modeling & Analysis

The course involves: (1) getting familiar with machine learning algorithms, their assumptions and their limitations; (2) learning how to select the training corpus and how to evaluate the outcome of training; (3) applying machine learning algorithms to a set of case studies, taken from recent research in Software Engineering. The course will cover unsupervised learning (e.g., clustering, feature maps), supervised learning (e.g., classifiers, neural networks) and concept mining (association rules, bayesian networks).

We live in a world where big amounts of data are generated by multiple sources. However, data is useless unless it is properly modelled and analyzed. This course will teach students how to turn data into information, i.e., processed and organized data with meaning, by automated identification of rules, patterns and regularities.

In presence

Students will be involved in practical exercises and will experiment with the presented techniques by applying them to the course projects.

Optional written mid-term exam; final oral exam; optional homework; two mandatory projects.