Study-unit INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Course name Informatics
Study-unit Code A000701
Curriculum Comune a tutti i curricula
Lecturer Valentina Poggioni
Lecturers
  • Valentina Poggioni
Hours
  • 47 ore - Valentina Poggioni
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.