OVERVIEW

This course will analyze solutions that Artificial Intelligence and other innovative technologies have produced for the protection, use and enhancement of cultural heritage. We also want to stimulate new methodological and technological solutions to allow catalysing possible interactions and aggregations between the various subjects involved in developing new applications in the cultural heritage sector.

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of the course is to analyze solutions that Artificial Intelligence and other innovative technologies have produced for the protection, use and enhancement of cultural heritage. We also want to stimulate new methodological and technological solutions to allow catalysing possible interactions and aggregations between the various subjects involved in developing new applications in the cultural heritage sector. Reproducing cultural sites and making them usable in a different way, including through the web, means opening up new development opportunities for the civil and economic growth of the territories. Hence the need to define roles and connections, where only research and new technologies can suggest competitive paths and solutions that integrate tourism and culture on the one hand, with business and the market on the other.

PREREQUISITES

Basic knowledge of functional and object-oriented programming.

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., performs in first person, guided activities in the laboratory. 

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

The laboratory will be taught by the teaching docene, assisted by laboratory tutors. The laboratory activities will be held at the Computer Science Laboratory on the Savona Campus, and students will be divided into groups according to the capacity of the laboratory. Students will be required to make a reservation for the lab activity through the course portal. Only those who have made the reservation will have access to the lab activities. The organization and dates of the laboratory activities will be communicated directly by the lecturer at the beginning of the lectures and sdaranno 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
Smart agents
Problem Solving
Constraint satisfaction problems
Knowledge Representation
Natural Language
Visual perception and artificial vision
Python programming language

RECOMMENDED READING/BIBLIOGRAPHY

The materials used during the lessons in the virtual classroom and during the virtual laboratory activities will be made available as the course progresses on the AulaWeb portal in the Materials used in class section, together with links to resources and texts available online.

TEACHERS AND EXAM BOARD

Exam Board

GIOVANNI ADORNI (President)

GIANNI VIARDO VERCELLI

ILARIA TORRE (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/9913/p/studenti-orario

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 individually or as a group a thematic project assigned by the teacher during the course.

Exam schedule

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).