Unit INTRODUCTION TO ARTIFICIAL INTELLIGENCE
- Course
- Informatics
- Study-unit Code
- A000701
- Curriculum
- In all curricula
- Teacher
- Valentina Poggioni
- Teachers
-
- Valentina Poggioni
- Hours
- 47 ore - Valentina Poggioni
- CFU
- 6
- Course Regulation
- Coorte 2023
- Offered
- 2025/26
- Learning activities
- Caratterizzante
- Area
- Discipline informatiche
- Academic discipline
- 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.