INTRODUCTION TO ARTIFICIAL INTELLIGENCE
cod. 1009161

Academic year 2022/23
2° year of course - First semester
Professor
MORDONINI Monica
Academic discipline
Sistemi di elaborazione delle informazioni (ING-INF/05)
Field
Ingegneria informatica
Type of training activity
Characterising
48 hours
of face-to-face activities
6 credits
hub: PARMA
course unit
in ITALIAN

Learning objectives

An introduction to symbolic and subsymbolic artificial intelligence
Methods for developing an intelligent system.
The knowledge of useful tools for the analysis and development of a process
data analytics

Prerequisites

No propedeutic courses. However, Students should have knowledge of
programming (especially python).

Course unit content

1 Introduction (2 hours)
2 Birth of the AI (2 hours)
3 The formal systems (2 hours)
4 Problem solving and the search for a solution (6 hours)
5 Knowledge-based systems (4 hours)
6 The rational agent (2 hours)
5 Formal logic as a language to develop intelligent systems (6 hours)
6 Prolog and backtracking (6 hours)
7 Hints of planning (4 hours)
8 introduction to subsymbolic intelligence (2 hours)
2 Business Intelligence and Descriptive Analysis (4 hours)
9 Hints of Machine Learning (8 hours)
19 Case Studies (2 hours)

Full programme

1 Introduction (2 hours)
2 Birth of the AI (2 hours)
3 The formal systems (2 hours)
4 Problem solving and the search for a solution (6 hours)
- solution space -
blind and informed research strategies
5 Knowledge-based systems (4 hours)
- definition of knowledge
the use of reasoning to infer new knowledge and plan actions
6 The rational agent (2 hours)
5 Formal logic as a language to develop intelligent systems (6 hours)
- boolean logic
- logic of predicates
6 Prolog and backtracking (6 hours)
- the prolog
- backtracking in prolog
7 Hints of planning (4 hours)
-structured planning
- continuous planning
8 introduction to subsymbolic intelligence (2 hours)
2 Business Intelligence and Descriptive Analysis (4 hours)
-What is business intelligence
-Descriptive analysis
9 Hints of Machine Learning (8 hours)
- the learning phase
- decision trees
- outline of neural networks
19 Case Studies (2 hours)

Bibliography

Material provided in class

RUSSELL, Stuart J.; NORVIG, Peter. Intelligenza artificiale. Un approccio moderno. Pearson Italia Spa, 2005.

Teaching methods

Lectures and laboratory exercises.
The lectures will cover the theoretical aspects of the course subjects.
Practical exercises related to real problems will be carried out in
laboratory

Assessment methods and criteria

There are no mid-term tests.
The exam consists of a written test with theory and practice questions.

Other information

Course notes and teaching materials will be distributed during the course
in electronic form.