Computer Science

This course introduces the social, ethical, legal, and professional issues involved in the widespread deployment of information technology. It stimulates students to develop their own, well-argued positions on many of these issues.

Students think about the social and ethical implications of the widespread and sustainable use of IT; develop awareness of the laws and professional codes of conduct governing the IT industry; explore IT industry working practices, including the need for continuing professional development; develop information gathering skills; and adopt principled, reasoned stances on important issues in the topic area. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor. The course consists of theoretical lessons and practical sessions. In each lesson, after a theoretical introduction, a practical session takes place in which the student is asked to experience the introduced topic first-hand. The course is organized in two modules. The first module covers basic programming concepts, the second module covers advanced topics. The topics of the lectures include:

• Introduction to programming
• Introduction to the Python language
• Importing and Exporting data and text in Python
• Manipulating data and text in Python
• Describing and visualizing data in Python
• Libraries for Machine Learning

At the end of the course, the student has competences on theoretical and practical foundations for the acquisition, manipulation, and analysis of text and data using computational tools. Furthermore, the student will be familiar with the methodological foundations for the development of scripts for natural language processing. They know and use the fundamental algorithms and data structures and are able to build and interpret graphs that show descriptive statistics of the data collected in order to facilitate its analysis.

This course provides a basic introduction to machine learning (ML) and artificial intelligence (AI). With an algorithmic approach, it offers a practical understanding of the methods that are reviewed, not least through their own implementation of several of the methods. The course covers supervised classification based on, for example, artificial neural networks (deep learning), in addition to unsupervised learning (cluster analysis), regression, optimization (evolutionary algorithms and other search methods) and reinforcement learning, as well as design of experiments and evaluation. The course also provides an introduction to philosophical fundamental problems and ethical issues related to ML/AI, in addition to the history of the field.

This course provides a comprehensive introduction to Python programming, focusing on fundamental concepts and techniques. It is designed for beginners and covers key topics such as data types, variables, control structures, functions, and file handling. The course exposes students to libraries like NumPy for numerical computations and Matplotlib for data visualization. Through hands-on exercises and projects, students will develop problem-solving skills; explore foundational elements of object-oriented programming, and learn to create reusable, efficient Python code. By the end of the course, students will have the skills to write clear, efficient, and reusable Python code as well as a strong foundation for further study in programming. 

This course provides sound understanding of fundamental concepts and emerging problems in networking and provides training in network programming.  Students will learn how to explain in detail how a piece of information travels through the Internet and reaches the other side of the world. 

Topics include emerging issues around the Internet, basic network programming for sockets, TCP, and routing. Prerequisite: basic programming skills in C/C++. 

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.