2021/2022

2023/2024

DM270

INF/01 (COMPUTER SCIENCE)

DEPARTMENT OF MATHEMATICS "FELICE CASORATI"

ARTIFICIAL INTELLIGENCE

PERCORSO COMUNE

3°

1st semester (02/10/2023 - 21/01/2024)

6

56 lesson hours

English

WRITTEN AND ORAL TEST

PASI GABRIELLA (titolare) - 2 ECTS
PEIKOS GEORGIOS - 2 ECTS
PINKOSOVA ZUZANA - 2 ECTS

Basic knowledge of statistics, programming languages, and machine learning.

The aim of the course is to provide an introduction to the fundamental concepts, models and techniques related to Information Retrieval Systems (aka Search Engines) and to Recommender Systems. These two categories of systems are nowadays largely diffused, and they offer an automatic support for the access to information potentially useful (relevant) to specific users’ needs.
While search Engines require users to explicitly express their information needs by formulating a query (pull technology), Recommender Systems do not require an explicit users’ actions, as they provide users with information/services of potential relevance to them, based on user profiles (push technology).
After successfully completing the course, students will be able to:
- Understand the basic structure of search engines and recommender systems
- Know the basic models at the basis of both categories of systems
- Describe the main challenges behind these technologies

This course will provide an introduction to Information Retrieval and to Information Filtering.
The course will then introduce the Information Retrieval pipeline, the main components of an Information Retrieval Systems, and the main Information Retrieval models. Then the main categories of Recommender Systems will be introduced (content-based, collaborative and knowledge based), and the cold start problem will be introduced. Open-source software for designing search systems and recommender systems will be introduced and employed.

The course will be constituted of both lectures introducing the main topics and laboratory sessions where open source tools will be explained and employed. Seminars held by experts at national and international levels will be part of the course.

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008.

D Jannach, M Zanker, A Felfernig, G Friedrich Recommender Systems: an Introduction, Cambridge University Press, 2010.

Written and optional oral individual examination, definition of a laboratory project that can be developed also by groups of students (up to three students).
The written examination is aimed at assessing the level of understanding of the basic aspects taught during the course; it is constituted by a set of open questions.
The goal of the group project is the usage of open-source software that will be employed to develop technological solutions to the problems addressed in the course.