To acquire specific knowledge on the methodologies that allow to express in a simple and efficient way the deterministic and random transformations that information undergoes through physical systems. To acquire specific knowledge on methodologies that allow to analyze the performance of simple transmission systems and networks of information processing. To acquire basic knowledge for artificial intelligence applications to data, signals and images. To know how to connect the different functional blocks that build an information and communication system in a single framework of integrated and interdependent processes. To provide an overview of the main information and communication technologies, briefly describing both fundamental operational concepts and typical application examples, as well as the economic implications of these systems in the digital society.
teacher profile teaching materials
A historical overview of the information age.
The accelerating pace of technological change.
Digital transformation and innovation.
The impact of digital technologies on the economy and society.
Telecommunication Systems
Information transmission and processing systems.
Basic technical aspects and economic implications.
Fundamentals of telecommunication systems.
Introduction to telecommunication networks.
Cellular communication systems.
The evolution of telecommunication networks and services: from 2G to 5G.
Technologies, systems, and platforms for the Internet of Things.
The role of telecommunication infrastructures in the digital economy.
Artificial Intelligence
Overview and main trends in artificial intelligence.
Digital transformation and artificial intelligence.
History and definitions of AI.
Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).
Generative and conversational artificial intelligence.
Introduction to artificial neural networks.
The role of data: Big Data and the Internet of Things.
Models, capabilities, and limitations of artificial intelligence systems.
Machine Learning and Generative AI
Main machine-learning paradigms.
Supervised learning.
Unsupervised learning.
Reinforcement learning.
Training, optimisation, and generalisation.
Fundamentals of natural language processing.
Language models and conversational generative AI.
Economic, Social, and Ethical Implications of AI
Biases, errors, responsibility, and accountability.
Opportunities and risks of artificial intelligence in the digital economy.
The impact of AI on economic and organisational processes.
Artificial intelligence and the transformation of work.
Technological evolution, disruptive innovation, and emerging skills.
Skills development through upskilling and professional reskilling.
Lecture notes, presentations, and additional learning materials provided by the lecturer and made available through the Moodle and Microsoft Teams platforms.
Programme
The Information Age and Digital TransformationA historical overview of the information age.
The accelerating pace of technological change.
Digital transformation and innovation.
The impact of digital technologies on the economy and society.
Telecommunication Systems
Information transmission and processing systems.
Basic technical aspects and economic implications.
Fundamentals of telecommunication systems.
Introduction to telecommunication networks.
Cellular communication systems.
The evolution of telecommunication networks and services: from 2G to 5G.
Technologies, systems, and platforms for the Internet of Things.
The role of telecommunication infrastructures in the digital economy.
Artificial Intelligence
Overview and main trends in artificial intelligence.
Digital transformation and artificial intelligence.
History and definitions of AI.
Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).
Generative and conversational artificial intelligence.
Introduction to artificial neural networks.
The role of data: Big Data and the Internet of Things.
Models, capabilities, and limitations of artificial intelligence systems.
Machine Learning and Generative AI
Main machine-learning paradigms.
Supervised learning.
Unsupervised learning.
Reinforcement learning.
Training, optimisation, and generalisation.
Fundamentals of natural language processing.
Language models and conversational generative AI.
Economic, Social, and Ethical Implications of AI
Biases, errors, responsibility, and accountability.
Opportunities and risks of artificial intelligence in the digital economy.
The impact of AI on economic and organisational processes.
Artificial intelligence and the transformation of work.
Technological evolution, disruptive innovation, and emerging skills.
Skills development through upskilling and professional reskilling.
Core Documentation
F. Benedetto, Chi ha paura dell’IA? 15 domande per conoscere, dominare l’IA e navigare consapevolmente tra le sue opportunità e i suoi rischi, Rome, Edizioni Lavoro, 2026.Lecture notes, presentations, and additional learning materials provided by the lecturer and made available through the Moodle and Microsoft Teams platforms.
Attendance
Attendance is not mandatory but is strongly recommended to support a better understanding of the course topics and participation in discussions, in-depth analyses, and case studies. Non-attending students are required to prepare for the examination by studying the recommended textbook and all learning materials provided by the lecturer through Moodle and Microsoft Teams.Type of evaluation
Learning outcomes are assessed through a written examination consisting of three open-ended questions covering the topics addressed during the course. The examination is designed to assess: knowledge and understanding of the course content; the ability to connect technological, economic, and social aspects; the ability to critically analyse the opportunities, limitations, and risks of the technologies studied; clarity of presentation and appropriate use of subject-specific terminology. The final grade is expressed on a scale of 30. A minimum grade of 18/30 is required to pass the examination. Honours may be awarded for complete and in-depth answers demonstrating exceptional clarity, critical analysis, and the ability to establish meaningful connections among the course topics.