Artificial Intelligence

University of Cagliari

Academic year: 2024-2025

Instructors: Prof. Giorgio Fumera, Dr. Ambra Demontis

MSc programs in:

• Computer Engineering, Cybersecurity and Artificial Intelligence (course description)
• Electronic Engineering (course description)
• Internet Engineering (course description)

Lecture schedule

• Tue, Oct. 1: Introduction to the Artificial Intelligence field. Graph search algorithms
• Wed, Oct. 2: Graph search algorithms
• Thu, Oct. 3: Machine learning
• Tue, Oct. 8: Graph search algorithms
• Wed, Oct. 9: Graph search algorithms
• Thu, Oct. 10: Machine Learning
• Tue, Oct. 15: Graph search algorithms
• Wed, Oct. 16: Logical languages for knowledge representation and reasoning
• Thu, Oct. 17: Machine Learning
• Tue, Oct. 22: Logical languages for knowledge representation and reasoning
• Wed, Oct. 23: Logical languages for knowledge representation and reasoning
• Thu, Oct. 24: Machine Learning
• Tue, Oct. 29: Logical languages for knowledge representation and reasoning
• Tue, Nov. 5: Machine Learning
• Wed, Nov. 6: Logical languages for knowledge representation and reasoning
• Tue, Nov. 12: Uncertain knowledge representation and reasoning: Bayesian networks
• Wed, Nov. 13: Uncertain knowledge representation and reasoning: Bayesian networks
• Thu, Nov. 14: Machine Learning
• Tue, Nov. 19: Machine Learning
• Wed, Nov. 20: Uncertain knowledge representation and reasoning: Bayesian networks
• Thu, Nov. 21: Machine Learning
• Tue, Nov. 26: Machine Learning
• Wed, Nov. 27: Machine Learning
• Tue, Dec. 3: Machine Learning
• Wed, Dec. 5: Machine Learning
• Tue, Dec. 10: Machine Learning
• Wed, Dec. 11: Uncertain knowledge representation and reasoning: Bayesian networks
• Thu, Dec. 12: Machine Learning
• Tue, Dec. 17: Machine Learning
• Wed, Dec. 12: Uncertain knowledge representation and reasoning: Bayesian networks

Lecture slides

Introduction to AI

Part I – Problem-solving, Knowledge and Reasoning (G. Fumera)

1. Graph Search Problems
2. Knowledge Representation and Reasoning: Logical Languages
3. Uncertain Knowledge Representation and Reasoning: Bayesian Networks (1st part)

Part II – Machine Learning (A. Demontis)

1. Introduction to Machine Learning
2. Decision Trees (soon available)
3. Performance Evaluation (soon available)
4. Neural Networks (soon available)
5. Ensemble Methods (soon available)

   Exercises

6. Graph Search Problems (soon available)
7. Logical Languages for Knowledge Representation and Reasoning (soon available)
8. Bayesian Networks for Uncertain Knowledge Representation and Reasoning (soon available)
9. Machine Learning (soon available)