AI in Everyday Life
Welcome to the homepage of the winter 2024 edition of AI in Everyday Life course of the Master in Communication of Science and Innovation (Scicomm) at the University of Trento.




The course will start on Tuesday 9th, 6:00PM

The lesson on Thursday 18th has been cancelled. To catch up, there will be an extra lesson on February 20th. Sorry for the inconvenience.

January 12th, 2024





Last modification: January 7th, 2024


The course will be taught using the distance learning method, with sessions taking place fully online via Zoom. At the first meeting (Tuesday, 9th of January, 17.30) we will discuss the logistics with you and present an overview of the course. The lectures will occur following the scheduling indicated in the Calendar and Material section. The course material includes slides, demo videos, support resources and links, which are provided on the web site. After each lesson, there will be a Q&A session during which the students can ask questions about their curiosities and doubts. Each week, you will find on the platform the following learning materials:

  • The learning objectives for the unit
  • The reading materials
  • A video presentation
  • A quiz that you can use for self-evaluation
  • Please study the materials before attending the respective class meeting, so that we can use our time together for interactive activities and discussion. Attendance at the meetings is mandatory, although you may, of course, watch the video presentations at any time that is convenient to you.


    Course Objectives and Outcomes

    Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution, transforming all sectors of society, from business and health to education. Targeted or Restricted AI (Narrow AI), applications aimed at carrying out very specific tasks, have been evolving more and more in recent decades. From the use of technologies such as Siri or Alexa which help us make a phone call, or the use of the Google search engine which returns us the information we requested in a few seconds or even when we discover photos on social networks where we use targeted applications AI. In this course, we utilize the participants’ familiarity with such applications, in order to explore the technical, but also social and ethical aspects, of the modern AI system. Narrow AI differs fundamentally from the General AI (e.g., humanlike agents and even humanoid robots) that are often portrayed in science fiction novels and movies, which tends to influence the public’s beliefs about what AI is and where AI research is headed in the near future. In fact, General AI is – according to most scientists – neither possible nor desirable. Thus, this course focuses on Narrow AI applications, which can augment human capabilities, and which are playing an increasing role in the workplace. The overall objective of the learning unit is to help participants appreciate what AI actually is, as well as to understand the basic elements of AI technologies, and how they are developed.

    General Description

    Learners will familiarize themselves with the fundamental definitions and concepts around AI, but also the widespread methods used in the creation of systems. In particular, an introduction to data-driven AI, based on machine learning, is provided. The role of personal data in the AI ecosystem – provided to companies via the use of services such as search engines and social media – will be discussed. In addition to examining the fundamental concepts surrounding the technical aspects of Narrow AI, we will also examine its widespread use's ethical and social implications. Participants will therefore learn to analyze the functions of "everyday" AI applications and will be able to understand the potential risks of these technologies, in an effort to raise awareness, encouraging them to critically evaluate them. In short, the ultimate goal of this course is to empower participants to use AI applications – particularly those that are playing an increasing role in everyday life – in an optimal way. The course will cover the following topics:
    • Machine Learning and Big Data
    • Natural Language Processing
    • Personalization
    • Ethical issues in AI applications



    The course is designed to be introductory. Students from all types of backgrounds are welcome!


    Course modality

    The course will be taught in English. The course will be fully online, including the evaluation tests. The lecture hours will be used to answer the students’ questions and to collect feedback


    Matteo Busso
    Lecturer #1

    Calendar and Material

    The course runs from January 9th, 2024 till February 6th, 2024 with the following schedule


    • Tuesday, 17:30-19:00, Online


    • Thursday, , 17:30-19:00, Online


    You might want to read the Instructions to understand how to take the course.



    Lesson Number Date                                Time Lesson title                              Material                         Other Resources                         Assignment                 Quiz        
    1 Tue 09 Jan, 2024 18:00 Introduction Slides - Live Session
    2 Thu 11 Jan, 2024 17:30 Exploring AI applications Slides - Video - Live Session AI HLEG Quiz 1
    3 Tue 16 Jan, 2024 17:30 Machine Learning and Big Data Slides - Video - Live Session IBM_ML Assignment 1 Quiz 2
    4 Thu 18 Jan, 2024 17:30 Cancelled
    4 Thu 23 Jan, 2024 17:30 Computer Vision and Face Recognition Slides - Video - Live Session AI Replicability - C.V. Errors Assignment 2 Quiz 3
    5 Tue 30 Jan, 2024 17:30 Natural Language Processing Slides - Video - Live Session NLTK - TheGuardian Assignment 3 Quiz 4
    6 Tue 06 Feb, 2024 17:30 Personalization Slides - Video - Live Session . Quiz & Assignment 4 .
    7 Tue 20 Feb, 2024 17:30 AI and Ethics Slides - Video - Live Session AI Human Impact - TrustworthyAI Quiz & Assignment 5 .


    The course evaluation is either pass or failed and it will be based on attendance and assigment fullfilment. Attendance is mandatory for a minimum of 70% of the hours of frontal teaching and is strongly suggested given that this is a hands-on lab course. In addition to attendance, the final evaluation will be based on the completion of the activities proposed during the course.