Artificial Intelligence: a modern approach
Programme
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, The History of AI ,The State of the Art, Risks and Benefits of AI)
2. Knowledge, Reasoning and Planning overview (from chapters 7&10) - 2h
3. Knowledge Representation and First-Order Logic (from chapters 8,9&12)
4. Uncertainty and Probabilistic Reasoning (from chapters 13&14) - 2h
5. Machine Learning: Learning from Example – (from chapter 19) - 2h
6. Machine Learning: Learning probabilistic models (from chapter 20) - 2h (reading1)
7. Machine Learning: Deep Learning: Simple and Convolutional Networks (from chapter 21) - 2h (reading2)
8. Machine Learning: Deep Learning: Recurrent Networks (from chapter 21) - 2h (reading3)
9. Machine Learning: Reinforcement Learning: (from chapter 23 sections 1-3) (reading4)
10. Perceiving and Acting: Deep Learning in NLP transformers) (from chapters 24&25) - 2h (reading5)
11. Ethical AI and Human Centered AI (from chapter 27) - 2h
Educational aims
The educational objectives of the course are to provide students with a reasoned and comprehensive overview of Artificial Intelligence, enabling them to gain a significant understanding of the core ideas and techniques underlying the design of intelligent computer systems. Starting from a historical perspective, the course explores the main areas of AI: reasoning, learning, perceiving, acting, and learning, (including machine learning and deep learning). The curriculum also addresses critical aspects such as ethical and human-centered AI, emphasizing problem formalization rather than coding. Ultimately, the techniques and knowledge acquired will serve as a solid foundation for further studies in AI and for future applications in AI-based fields.