Computer Science

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. This course focuses on the main data mining methods used in knowledge discovery in business employing internal and external data. With an emphasis on data analysis and on the use of a software, special attention is devoted to techniques that help to single out the relationships of interdependence and patterns in business and market research phenomena. Students learn, hands-on, how to organize and analyze market research data. In particular, at the end of the course students are able to: independently run a complete data mining process (from data pre-processing to the interpretation of obtained results); choose the best suited statistical methodology for the problem at hand; to critically interpret empirical results.

The course content is divided as follows:

1. INTRODUCTION: data-analytic thinking, overview of Data Mining, from business problems to Data Mining tasks, the Data Mining process; real-world business challenges.

2. DATA EXPLORATION AND PREPARATION: data objects and attributes type, data matrices and their transformations, data cleaning.

3. STATISTICAL AND DATA MINING SOFTWARE: introduction to SAS; SAS LAB tutorial on data organization  and data preprocessing using real datasets.

4. MULTIDIMENSIONAL DATA ANALYSIS & DIMENSIONALITY REDUCTION: Principal component analysis and its variants (e.g., PCA of ranks); Multiple Correspondence Analysis - categorical pattern detection. Theory and practice with SAS.

5. PROXIMITY MEASURES: distance and similarity for mixed data.

6. CLUSTERING: hierarchical, partitional and hybrid clustering. Understanding the Results of Clustering.

7. PROFILING: deriving typical behavioral segments. 

8. CO-OCCURRENCES AND ASSOCIATIONS: Finding items that go together. Theory and application of main association rules algorithms in SAS.

9. Data Mining SCORING: Theory and practice.

10. Causal ML and Advanced Lab:  causal inference fundamentals; application of causal ML algorithms in the context of business analytics for decision support; evaluate a marketing campaign using causal ML in SAS; targeting and interpreting causal results.

This course provides students with a solid grounding in various aspects of software engineering process related to building large software systems. The course covers various aspects related to building software systems ranging from the use of software lifecycle models, to project management, to large-scale software architectures. Specifically, software lifecycle models, including variations of the waterfall and spiral models as well as extreme programming and agile, are introduced along with concepts that are relevant to the specific model stages. These concepts include domain analysis, requirements and specification analysis, testing and debugging, and version control. Moreover, strategies for managing large software projects and their contracts as well as project teams are presented and contrasted.

This course covers the process of bioinformatics data analysis and the interpretation of the results in a biological context. The following topics will be addressed in the course: command line usage; programming/scripting; current bioinformatics data analysis tools; and automated analysis pipelines. The first part of the course covers command line usage (linux), bioinformatics script programming (python), as well as the theory and tools required to analyze data produced by current sequencing technologies and interpret the results. Topics include genome assembly, sequence annotation, gene expression, biological networks, and comparative genomics. During the second part of the course, students - in teams - apply their knowledge in a small research project. Given a specific biological question and the required data, the goal is to build a data analysis pipeline and describe the biological interpretation. BIF20306 Introduction to Bioinformatics or SSB34306 Computational Biology and BIF21806 Practical Computing for Biologists or INF2306 Programming in Python required.

This course covers information security and alternatives for protecting secret information from malicious digital attacks. The course examines various information protection devices and the principles, mechanisms, and implementations of computer security,  

Topics include Security concepts and principles, Software security – exploits and privilege escalation, User authentication, Operating systems security, Access control, Secure design and coding exercises, Cryptographic building blocks, Malicious software, GitCTF Competition, Web and browser security, Open source security and more. 

This course introduces the general technical/methodological requirements, problems/challenges, and application possibilities of brain-computer interfacing. Besides attending lectures, in which course participants are provided with basic relevant knowledge by local BCI researchers, students study seminal papers of recent BCI work. Further, discuss the pros and cons of different functional brain imaging methods employed for BCIs as well as ethical implications and future directions. The practical part of this course includes a demonstration of an fNIRS-BCI experiment. At a later stage of the course, students perform an fNIRS-BCI experiment themselves.

This course teaches how to identify opportunities for innovation and develop user-centered, impactful, and innovative digital solutions that respond to real-world needs. Through a combination of theoretical insights and practical tasks, explore how new digital solutions can drive change across various industries and societal needs.

Work in teams on real-world problems, realize bold ideas, and develop MVPs (minimum viable products) with mentoring and supervision. Key skills include market analysis, requirement elicitation, innovation strategy, solution making, and effective pitching.

This course uses a human-centered lens to examine security and privacy, focusing on how design and research can create solutions that people can understand, trust, and use.  

Security and privacy are as much about people as they are about technology. Many failures arise not just from a lack of technical capability, but from mismatches with how people think, behave, and interact in their everyday contexts. 

Students engage with real-world topics ranging from authentication and security warnings to deceptive patterns, AI privacy, and privacy and security challenges in sensing environments, while learning foundational methods in user research and usable security and privacy evaluation. Through critical readings, class discussions, and hands-on projects, students develop skills to understand and design for human factors in security and privacy contexts. 

Key themes include: 1) Human-centered research methods for security and privacy, 2) Usable security tools, access control, and warnings 3) AI-enabled security and privacy challenges, 4) Sensing environments and security/privacy issues, and 5) Ethics and social implications in security and privacy 

This course provides a broad overview of the field of bioinformatics, with a focus on practical application and interpretation of results from tools used in everyday biological research. Assumed Knowledge in MAT15403 Statistics 2.

This course covers homology, cohomology and applications, CW-complexes, and basic notions of homotopy theory.

There are two distinct parts to this course. The first few lectures provide students with a general overview of connectionism: its origins as an attempt to model the functioning of the brain, and the various classes of algorithms created starting from these foundations. The second part focuses on the last 10-15 years. The course provides a general framework for designing machine learning models that deal with complex structured data, introduces graphical models and Bayesian networks, and describes inference and learning algorithms for them. The course also addresses the case of neural networks, i.e. to describe possible strategies for effectively training them in real-world scenarios.