Study-unit INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Course name | Informatics |
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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 2022 |
Supplied | 2024/25 |
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). The KDD process; Data Mining and Machine 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 data mining and in particular for data classification; she/he will be able to design a KDD system. |
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. The Knowledge Discovery process in Databases (KDD). Introduction to data mining; data mining and machine learning. Classification: decision tree (attributes and algorithms); NN classifiers; Naive Bayes classifiers; neural networks classifiers. Techniques and measures for model evaluation. Building and analysis of training and test sets. |