Themen der Lehrveranstaltung
• Artificial intelligence and agents. • Search space exploration. • Automated planning. • Data analysis. • Model selection. • Supervised learning. • Unsupervised learning. • Reinforcement learning. • Elements of deep learning

Unterrichtsform
Frontal lectures, homeworks, exercises, and laboratories

Bildungsziele
The course belongs to the type "caratterizzanti – discipline informatiche". Students gain an understanding of the theoretical and practical foundations and concepts of Artificial Intelligence and Machine Learning including artificial intelligence and agents, search space exploration, automated planning, data analysis, model selection, supervised learning, unsupervised learning, reinforcement learning, elements of deep learning Knowledge and understanding • Know the principles of machine learning and artificial intelligence and potentials and limits in various application domains. Applying knowledge and understanding • Be able to adopt programming techniques of artificial intelligence for state space search and planning; • Be able to adopt machine learning techniques to extract knowledge from data. Making judgments •Be able to work autonomously according to the own level of knowledge and understanding; • Be able to collect and interpret useful data and to judge intelligent systems and their applicability; Ability to learn •Have developed learning capabilities to pursue further studies with a high degree of autonomy. Communication skills •Be able to use one of the three languages English, Italian and German, and be able to use technical terms and communication appropriately. • Be able to structure and write technical documentation. • Be able to work in teams for the realization of intelligent systems. Learning skills: • Have developed learning capabilities to pursue further studies with a high degree of autonomy • Be able to follow the fast technological evolution and to learn cutting edge technologies and innovative aspects of last generation intelligent systems

Art der Prüfung
Oral exam and project work. The mark for each part of the exam is 18-30, or insufficient. The oral exam comprises verification questions, and open questions to test knowledge application skills. It counts for 50% of the total mark. The project consists of a deep learning project and verifies whether the student is able to apply the concepts taught or presented in the course to solve concrete problems. It is assessed through a final presentation, a demo, and a project report and can be carried out either individually or in a group of 2 students. It is discussed during the oral exam, and it counts for 50% of the total mark

Bewertungskriterien
The final mark is computed as the weighted average of the oral exam and the project. The exam is considered passed when both marks are valid, i.e., in the range 18-30. Otherwise, the individual valid marks (if any) are kept for all 3 regular exam sessions, until also all other parts are completed with a valid mark. After the 3 regular exam sessions, all marks become invalid. Relevant for the oral exam: clarity of answers; ability to recall principles and methods, and deep understanding about the course topics presented in the lectures; skills in applying knowledge to solve exercises about the course topics; skills in critical thinking. Relevant for the project: skill in applying knowledge in a practical setting; ability to summarize in own words; ability to develop correct solutions for complex problems; ability to write a quality report; ability in presentation; ability to work in teams.Non-attending students have the same evaluation criteria and requirements for passing the exam as attending students.

Pflichtliteratur

All the required reading material will be provided during the course and will be available in electronic format. Copy of the slides will be available as well

Weiterführende Literatur

Poole, D., Mackworth, A. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, 3rd Edition, 2023. ISBN: 9781009258197.

James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. Statistical Learning. In: An Introduction to Statistical Learning. Springer, 2023.

Bishop, C. M. Pattern Recognition and Machine Learning, Springer, 2006. ISBN: 0387310738,

Weitere Informationen
Software used: Python, Scikit-Learn, PyTorch