Artificial Intelligence: a modern approach

Academic year 2024/2025
Lecturer Francesco Giannini, Fosca Giannotti

Examination procedure

Seminar

Examination procedure notes

The students will be asked to realize readings and presentations along the course path

Prerequisites

The course is meant for undergraduate students with a scientific background, and familiarity with basic computer science concepts (algorithms, data structures, complexity) might be useful, however, the course will concentrate on the problem formalization, not on their coding. It aims to provide a basic introduction useful for further studies in AI and AI-based application areas.

Syllabus

This (20 hours) course is reasoned overview on Artificial Intelligence, it is based on the new revised edition of the mostly widely adopted textbook on AI: Artificial Intelligence: a modern approach, by Stuart Russell and Peter Norvig. The will concentrate on few selected topics with the aim to allow students to achieve a significant view on the basic ideas and techniques underlying the design of intelligent computer systems.  Starting from an historical perspective introduction the course will overview topics ranging over the four areas of AI: reasoning, learning, perceiving and acting with a special focus on learning. At the end of the course, the techniques learnt will serve as the foundation for further study in AI and in AI based application areas.Syllabus 

1. Introduction to AI – ( from chapters 1 & 2) – 2h (What Is AI, The Foundations of AI,a. The History of AI ,The State of the Art, Risks and Benefits of AI)

2. Knowledge, Reasoning and Planning overview (from chapters 7 & 10)

3. Knowledge Representation and First-Order Logic (from chapters 8,9 & 12)

4. Uncertainty and Probabilistic Reasoning (from chapters 13 & 14)

5. Machine Learning: Learning from Example – (from chapter 19)

6. Machine Learning: Learning probabilistic models (from chapter 20)

7. Machine Learning: Deep Learning: Simple and Convolutional Networks 

8. Machine Learning: Deep Learning: Recurrent Networks (from chapter 21)

9. Machine Learning: Reinforcement Learning: (from chapter 23 sections 1-3) 

10.  Perceiving and Acting: Deep Learning in NLP transformers) (from chapters 24 & 25)

11.Ethical AI and Human Centered AI (from chapter 27)


Bibliographical references

Artificial Intelligence: a modern approach, Fourth Edition, by Stuart Russell and Peter Norvig.http://aima.cs.berkeley.edu/ Global Edition