Study-unit INTRODUCTION TO ARTIFICIAL INTELLIGENCE
| Course name | Informatics |
|---|---|
| Study-unit Code | A000701 |
| Curriculum | Comune a tutti i curricula |
| Lecturer | Valentina Poggioni |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2023 |
| Supplied | 2025/26 |
| Supplied other course regulation | |
| Learning activities | Caratterizzante |
| Area | Discipline informatiche |
| Sector | INF/01 |
| Type of study-unit | Opzionale (Optional) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | Italian |
| Contents | Introduction to artificial intelligence.Turing's Test. Agent-reasoner approach. Agent Models. State Space Search. Uninformed search. Informed Heuristic search, A*. Properties of heuristics. Algorithms for two-player games (0-sum games). Introduction to Machine Learning. Supervised learning. Classification. Model evaluation methods and measures. Training and test sets analysis and building. |
| Reference texts | Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach, Global 4th Edition - Pearson - 2020 Pang-Ning Tan, M. Steinbach, A. Karpatne, V. Kumar - Introduction to data mining - Pearson - 2019 |
| Educational objectives | The student will acquire fundamental concepts of artificial intelligence systems and agents based models. Student will know main algorithm for uninformed and euristics state space search. He/she will be able to model and implement an agent based system as state space search problem, as well as algorithms for 2-players, 0-sum, games. The student will learn the main techniques and algorithms for supervised machine learning and in particular for data classification. |
| Prerequisites | Basic knowledge on Algorithms and Python programming. |
| Teaching methods | Face to face lessons in room and laboratory. |
| Other information | https://unistudium.unipg.it |
| Learning verification modality | Oral exam and project development |
| Extended program | Introduction to artificial intelligence.Turing's Test. Agent-reasoner approach/rational-human. Agent Models: reactive agent, simple agent with state, planning agent, tuility based agent. State Space Search: modelling problems. Uninformed search DFS, BFS, limited depth, uniform cost,. Informed Heuristic search, greedy, A*. Space/Time complexity of algorithms, equivalent branching factor. Properties of heuristics. Minimax algorithm and its optimized versions for 2-players, 0-sum, games. Introduction machine learning and in particular to supervised machine leraning. Classification: decision tree (attributes and algorithms); kNN classifiers; Naive Bayes classifiers; neural networks classifiers, both MLP and CNN models. Techniques and measures for model validation and evaluation. Building and analysis of training and test sets. |