The teaching is organized as follows:

Learning objectives

The corse aims to provide the theoretical and practical foundations for integrating data from, possibly, heterogeneous sources and the subsequent phase of extraction of summary information/knowledge. By completing the course, the students will be able to tackle complex data mining problems by designing and implementing a full pipeline that allows its user to integrate the necessary data sources, select and apply the adequate data mining techniques for solving a specific data mining problem, and evaluate its performances. Given a data mining problem, coming from a real-world domain ranging from industry to healthcare, the course enables the students to design, apply and test original solutions or or modifications of existing ones, for solving it and evaluate the feasibility of the proposed solution in a real environment.

Program

The course is structured in five main modules covering fundamental techniques for knowledge extraction and integration from data. It begins with an introduction to fundamental data mining concepts and an overview of the knowledge discovery process. The first main module is dedicated to Association Rules, exploring algorithms for frequent pattern identification, association rule analysis with practical examples on air quality data, and the use of p-values for statistical validation. The second module explores Mining Structures, focusing on data-driven approaches for process analysis, process mining, and automata learning. The third module covers advanced Machine Learning techniques, including classification, logistic regression, ensemble methods, post-pruning, k-means clustering, time-series analysis, conformal prediction, and boosting. There is a module that integrates Game Theory in data mining, exploring Nash equilibria, Shapley values, and their applications in model interpretation.

Learning assessment procedures

Learning assessment is based on assigned exercises during the course and a final oral examination. Students must complete all exercises proposed by the instructor, covering the different program topics: Association Rules, Mining Structures, Machine Learning, Game Theory for Data Mining, and Pattern Mining. No alternative proposals are provided - all assigned exercises are mandatory and must be submitted. During the oral examination, students will present the results of completed exercises and answer questions about the methods used, obtained results, and theoretical concepts underlying the applied data mining techniques.