Mario VENTO | INTELLIGENZA ARTIFICIALE: METODI ED APPLICAZIONI
Mario VENTO INTELLIGENZA ARTIFICIALE: METODI ED APPLICAZIONI
cod. 0612700146
INTELLIGENZA ARTIFICIALE: METODI ED APPLICAZIONI
| 0612700146 | |
| DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS | |
| EQF6 | |
| COMPUTER ENGINEERING | |
| 2024/2025 |
| YEAR OF COURSE 3 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 3 | 24 | LESSONS | |
| ING-INF/05 | 2 | 16 | EXERCISES | |
| ING-INF/05 | 1 | 8 | LAB |
| Objectives | |
|---|---|
| The course presents the main methods underlying artificial intelligence, emphasizing the design aspects related to the implementation of systems operating in real environments on applications that are currently central in the market (intelligent robotics, autonomous vehicles, computer vision, biometrics, etc.). The course introduces the methodological foundations of AI systems based on the knowledge representation paradigm and the learning paradigm. |
| Prerequisites | |
|---|---|
| Knowledge of the fundamentals of mathematical logic, programming, and object-oriented programming is required. |
| Contents | |
|---|---|
| The course includes 48 hours of teaching, comprising lectures, classroom exercises, and laboratory work on various systems (cognitive robots, autonomous vehicles, artificial vision systems, high-performance computing systems). The detailed course contents are as follows: **Introduction to Artificial Intelligence (AI) (4 hours)** - Historical background and evolution of AI - Typical problems addressed in AI applications - Main application areas **Architectural Models of Intelligent Systems (4 hours)** - Knowledge-based problem-solving methods - Representation in state space - Knowledge-based representations (rule-based and logic-based) **Representation in State Space (8 hours)** - Search strategies (informed and uninformed) - Game theory and adversarial search **Knowledge-Based Representation (10 hours)** - Logical agents, propositional logic, and logical inference - First-order predicate logic - Production rule systems - Knowledge-based systems **Introduction to AI Languages (10 hours)** - Prolog: from logic to logic programming - The Prolog language as a solver - Design and development of programs **Learning-Based Methods (10 hours)** - Classification of learning methodologies - Supervised learning - Representation methods - Optimized feature extraction algorithms - K-nearest neighbor classifier - Decision trees - Learning through neural networks **AI Applications in Engineering (2 hours)** - Performance evaluation - AI hardware architectures - Privacy issues - European AI regulations |
| Teaching Methods | |
|---|---|
| The course includes 48 hours of instruction, comprising lectures, classroom exercises, and laboratory work on various systems (cognitive robots, autonomous vehicles, artificial vision systems, high-performance computing systems). Students are required to integrate individual study with group work in developing a project on the topics covered in the course, thus enhancing their ability to apply the presented methodologies in real-world contexts. |
| Verification of learning | |
|---|---|
| The assessment consists of a written exam and a project work. The written exam aims to predominantly evaluate the knowledge of the methodologies and techniques of artificial intelligence covered in the course. The project work aims to assess the ability to apply this knowledge in real-world contexts using programming environments and AI system development tools. |
| Texts | |
|---|---|
| S. J. Russell, P. Norvig. “Artificial Intelligence: A Modern Approach,” Volume 1 (Third Edition, 2010) and Volume 2 (Second Edition, 2005), Pearson Education Italia. |
| More Information | |
|---|---|
| The course is held in Italian |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2024-11-18]
