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 2020
- Offered
- 2022/23
- 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.
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 Edition - Pearson - 2016
Pang-Ning Tan, M. Steinbach, A. Karpatne, V. Kumar - Introduction to data mining - Pearson - 2019 - Educational objectives
- The student will acquire fundamentak 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 proble. 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 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. 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.