Computer Science

Through the study of this course, students are required to master the basic theories and techniques of artificial intelligence for engaging in specific fields, especially It provides the necessary knowledge base for the development of artificial intelligence systems in the field of modern service industry and business intelligence.

Students are also required to understand the latest technologies, theories and methods of artificial intelligence development, and be able to choose suitable for the development of intelligent systems in specific fields technology and tools, and focus on mastering and mastering a certain type of key technology.

This course introduces computer programming in Python. Students learn modern programming concepts, problem solving and creation of computer applications using the Python programming language. Topics include basic Python language syntax, control flow, functions, lambda expressions, Python's common data structures, list comprehensions, file I/O and operating system interface, object-oriented programming, functional programming, and basic usage of common data science packages such as NumPy and Pandas.

In this course, students get an introduction to the concepts of database software, database design, management, and programming. This includes conceptual database design using the entity-relationship approach, logical database design, and physical database design. The course focuses on the relational data model. Students learn to design and implement a relational database using Structured Query Language (SQL), retrieve and manipulate data via SQL queries, normalize relational databases: normal forms, and the elimination of certain anomalies based on redundancy, tune database queries with security via permission rights and indexes, write stored procedures and triggers using procedural SQL, and use Java Database Connectivity libraries (JDBC) to access databases in Java programs. 

This course introduces methods for creating systems that use data intelligently to improve themselves. This requires combining human intelligence (using methods like crowdsourcing, collaborative design) with artificial intelligence (discovering which technology designs help which people) through designing randomized A/B experiments that are collaborative, dynamic, and personalized. The course requires students to take prerequisites.

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.