Computer Science

This is an introductory course with flat, broad and cutting-edge knowledge, which can meet the needs of cultivating talents in liberal arts to understand information technology from the level of principles and concepts, and establish necessary digital literacy from the perspective of computer culture.

This course covers the main concepts of data engineering, including data pipeline, data organization, efficient analysis of large data volumes, distributed data storage (depending on the system architecture, e.g., multi-core systems, multiprocessor systems, clusters), distributed and parallel data analysis, and map/reduce techniques and their generalization to distributed query processing. The course requires students to take prerequisites.

This course targets at helping students not only understand and master a specific research method, but also enhance the level of maturity of students towards academic research, more specifically the concepts, skills, and confidence required to learn new methods, or the “Data Quotient”. The lectures may cover basic Machine Learning, frontier methods in causal inference, and some Bayesian statistics. The course also aims at helping enrolled students develop research professionalism – the ability to be a good reader, listener, and speaker for the academia.

This course provides an introduction to different methods for supervised learning (regression and classification). The course contains both model- and algorithm-based approaches. The main focus is supervised learning, but unsupervised methods like clustering are briefly discussed. The course also deals with issues connected to large amounts of data (i.e. "big data"). The course gives a good basis for further studies in statistics or data science, but is also useful for students who need to perform data analysis in other fields.

This course focuses on data science analysis, for use in both research and the labor market, using the latest techniques applied to machine learning. It discusses parallel programming, hybrid programming, and distributed programming especially for big data. 

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 provides students with the advanced knowledge to the field of network analysis and its usages in other fields of research. At the end of the course, students gain knowledge on the Web as a socio-technical system involving specific processes, entities, and behaviors, using interdisciplinary methods that blend computer science, sociology, ethnography, economics, linguistics, etc. The students are able to analyze the Web phenomena similarly to typical objects from natural sciences, distinguishing between data and applications, agents from computationally generated profiles, and addressing the characteristics of networks of entities emerging from the informational, physical, social, and conceptual spaces constituting the Web.

This course examines the changing role of the operating system, the concept and implementation of process, the OS/hardware interface with regard to storage and protection, and the techniques developed to achieve safety and throughput in multitasking systems.

1) Teach the essence of software and the basic ideas and the main methods of software engineering systematically based on software lifecycle. 2) Organize the students to develop new software systems of medium size in groups by adopting a new generation of information technology and the new application mode. Guide the students to apply software engineering principles, methods, techniques and tools for software development, management and maintenance. 

This course provides a comprehensive introduction to the modern study of computer algorithms. The course uses Python language as a tool to learn various algorithms in depth. Knowledge in mathematics, especially algebra, is expected and having basic knowledge and experience in Python helps to better understand class content.

This course introduces the fundamentals of understanding user needs, designing prototypes, and evaluating user interfaces. It explores principles, techniques, and tools to create effective and intuitive interfaces, with a special focus on using AI tools. Students of all levels in design and coding skills are welcome.