Artificial Intelligence & Machine Learning

The AI&ML course consists of 2 modules: AI (6CFU) and ML (3CFU) tought by Prof. Patrizi.

Do not forget to register to Classroom! 

Prerequisites. Students taking this course should have knowledge of object oriented analysis, modeling and design, relational databases, and basic notions on probabilities, as acquired in previous courses. 

Objectives. The objective of the course is to introduce the basics of Artificial Intelligence and Machine Learning. The course will focus on transferring knowledge and capability to the "machine" (the intelligent agent) so as to make it able to understand the world and aswer questions about it, as well as to act on it, to accomplish desired tasks. The course will  present the basic techniques developed in the field of Artificial Intelligence, concerning modeling the world and exploiting the model for reasoning and planning, focusing on discrete models. It will also present the basic concepts and techniques concerning Machine Learning for learning from real world data or from simulations, how to answer and how to act on them.

Teaching material. 

[1] Course slides, notes, and additional material available on this site. 

[2] Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russell, Peter Norvig, Pearson 2020 (selected chapters). 

[3] Reinforcement Learning: An Introduction, 2nd Edition by Richard S. Sutton and Andrew G. Barto. MIT Press, Cambridge, MA, 2018. (selected chapters). Freely available here.