- Docente: Michela Milano
- Credits: 8
- SSD: ING-INF/05
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Computer Engineering (cod. 0937)
Learning outcomes
At the end of the course the students are able to use the main AI techniques to develop tools for solving real life applications. The students are able to understand and apply a wide range of techniques such as constraint programming, symbolic and sub-symbolic machine learning techniques, planning and swarm intelligence.
Course contents
PLANNING
- Non-linear planning
- Conditional planning
- Graph-based planning
- Planning for robotics
MACHINE LEARNING: symbolic and sub-symbolic approaches
- Decision trees - random forests
- Neural networks
- Bayesian approaches
- Inductive logic programming
OPTIMIZATION
- Constraint Programming and Global constraints
- Search strategies
- Applications
SWARM INTELLIGENCE
- Ant colony
- Bee Colony
- Particle Swarm Optimization
Readings/Bibliography
S. J. Russel, P. Norvig: "Artificial Intelligence: A modern approach", Prentice Hall International. Execrises on-line [http://aima.cs.berkeley.edu/] .
M.Ginsberg: "Essentials of Artificial Intelligence", Morgan Kaufman,1993.
P. H. Winston: "Artificial Intelligence: Third Edition", Addison-Wesley, 1992.
Teaching methods
Lectures and laboratory exercises
Assessment methods
Written exam
Teaching tools
Slides of lectures
Tools in lab
Office hours
See the website of Michela Milano
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.