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CODE 90636
ACADEMIC YEAR 2024/2025
CREDITS
SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05
LANGUAGE English
TEACHING LOCATION
  • SAVONA
SEMESTER 1° Semester

OVERVIEW

The course explores the intersection between artificial intelligence (AI) and the humanities, stimulating new methodological and technological solutions. It focuses on symbolic, subsymbolic, and generative AI, promoting interactions among subjects engaged in emerging applications. A section of the course is dedicated to practical labs, where students use the Python programming language and generative AI to develop applications in the Digital Humanities. These labs provide practical skills, preparing students for various roles in the digital world. In summary, the course aims to train professionals capable of using AI, the Python programming language, and other innovative technologies to navigate the challenges and opportunities of the evolving digital world.

AIMS AND CONTENT

LEARNING OUTCOMES

Obiettivo del corso è quello di analizzare soluzioni che l’Intelligenza Artificiale e altre tecnologie innovative hanno prodotto per la tutela, fruizione e valorizzazione del patrimonio culturale. Si vuole inoltre di stimolare nuove soluzioni sia metodologiche che tecnologiche per consentire di catalizzare possibili interazioni e aggregazioni tra i vari soggetti impegnati a sviluppare nuove applicazioni nel settore dei beni culturali. Riprodurre siti culturali e renderli fruibili in modo diverso, anche attraverso il web, significa aprire nuove possibilità di sviluppo per la crescita civile ed economica dei territori. Nasce quindi la necessità di definire ruoli e connessioni, dove solo la ricerca e le nuove tecnologie possono suggerire percorsi e soluzioni competitive che integrino turismo e cultura da un lato, con impresa e mercato da un altro.

AIMS AND LEARNING OUTCOMES

The course ‘Artificial Intelligence for Digital Humanities’ is designed to provide students with an understanding of artificial intelligence (AI) technologies and languages, with a particular focus on both symbolic AI, subsymbolic and generative AI. The course focuses on the field of Digital Humanities, stimulating the development of new methodological and technological solutions.

Knowledge and understanding: Students will gain knowledge of the fundamental principles of AI, generative AI, and the Python programming language. They will be able to understand how these technologies can be applied to Digital Humanities, not only for the enhancement of cultural heritage but also to stimulate innovation in emerging sectors.

Applying knowledge: Students will have the opportunity to apply their knowledge during laboratory activities, using Python and generative AI environments to develop practical applications. This hands-on approach will allow students to acquire practical and applied skills, preparing them for a variety of professional roles in the digital world.

Making judgenents: The course will encourage students to use generative AI and Python to create new ways of interacting with data, interpreting information, and generating new knowledge. This will stimulate their critical thinking ability, allowing them to draw informed conclusions and solve problems creatively and innovatively.

Learning slills: The course will promote a learning approach that is both autonomous and collaborative. Students will be encouraged to experiment, ask questions, and seek solutions, thus developing their ability to learn independently. At the same time, they will work in teams, learning the importance of collaboration and effective communication.

Learning Outcomes: At the end of the course, students will be able to use AI methodologies and techniques, Python, and other innovative technologies to develop applications in Digital Humanities. They will have a proper understanding of AI principles and will be able to apply this knowledge practically. They will also be able to work both independently and in teams, and will have developed good critical thinking and problem-solving skills.

PREREQUISITES

Basic knowledge of programming in Python.

TEACHING METHODS

The course is organized according to two distinct categories of activities:

  • Theoretical Lecture (Lecture - Lecture): a teaching activity in which the student is predominantly "passive," i.e., attends a theoretical or practical-application lecture in the classroom, or through the tools provided by the teaching portal.
  • Practical Lesson (Hands-on experience - Study session): component of "assisted teaching" in which the student is predominantly "active," i.e., carries out guided activities in the laboratory in person. 

Class attendance, the materials used, and the exercises are all essential elements for proper preparation in this discipline. Therefore, it is recommended to attend the lectures and exercises, to carefully read and scrupulously follow the directions provided in the materials made available online on the teaching portal. 

The laboratory will be held by the teacher in charge of the course, assisted by laboratory tutors. The laboratory activities will be carried out at the Computer Laboratory of the Savona Campus, and students will be divided into groups according to the capacity of the laboratory itself. Students will be required to book laboratory activities through the course portal. Only those who have made a reservation will be able to access the laboratory activities. The organization and dates of the laboratory activities will be communicated directly by the teacher at the beginning of the lessons and will be available on the course portal.

The teaching organization scheme results in 6 CFUs for a total of 150 hours of study-work.

SYLLABUS/CONTENT

Artificial Intelligence: Paradigms and History

  • Intelligent agents
  • Searching and Problem Solving
  • Constraint satisfaction problems
  • Knowledge Representation
  • Natural Language Processing
  • Learning
  • Visual perception and artificial vision
  • Large Language Models and Generative Artificial Intelligence
  • Ethics, safety, sustainability, and regulations of Artificial Intelligence
  • Python programming language [Data Types - Expressions and Output - Decision  and Iterative Structures - Functions - Files, Lists, Tuples, Strings - Dictionaries, Sets, Classes, Objects - Applications for Digital Humanities - API for GPT]

RECOMMENDED READING/BIBLIOGRAPHY

  • Stuart J. Russell, Peter Norvig, Artificial Intelligence. A modern approach, 4th. Ed. MyLab - Pearson, 2021.
  • Materials used during the classroom lessons and during the laboratory activities made available as the course progresses on the AulaWeb portal in the section ‘Materials used in class’, together with links to resources and texts available online.

LESSONS

LESSONS START

https://easyacademy.unige.it/portalestudenti/

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

In order to take the exam, the student must register online through the Student Portal at:   https: //servizionline.unige.it/studenti/.

The exam consists of an individual interview on the course program and on the discussion of the project carried out.

ASSESSMENT METHODS

In order to pass the exam, the student must:

- to produce a Disciplinary Thematic Glossary: ​​for each lesson the student is asked to create (and gradually enrich and refine) a glossary of disciplinary keywords;
- to develop a thematic project assigned by the teacher during the course.

FURTHER INFORMATION

Students with disabilities or learning disorders are allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the Delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities. Students are invited to contact the teacher of this course and copy the Delegate (https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita.html).

Agenda 2030 - Sustainable Development Goals

Agenda 2030 - Sustainable Development Goals
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