Silvia SCARPETTA | ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE DEVELOPMENT
Silvia SCARPETTA ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE DEVELOPMENT
cod. 0512900031
ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE DEVELOPMENT
| 0512900031 | |
| DEPARTMENT OF PHYSICS "E. R. CAIANIELLO" | |
| EQF6 | |
| SCIENCE AND NANOTECHNOLOGY FOR SUSTAINABILITY | |
| 2024/2025 |
| YEAR OF COURSE 3 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| FIS/02 | 6 | 48 | LESSONS |
| Objectives | |
|---|---|
| THE COURSE AIMS TO INTRODUCE THE FUNDAMENTAL CONCEPTS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS FOR SUSTAINABLE DEVELOPMENT. KNOWLEDGE AND UNDERSTANDING THE COURSE AIMS TO PROVIDE STUDENTS WITH THE NECESSARY TOOLS TO UNDERSTAND FUNDAMENTAL CONCEPTS IN THE FIELD OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE, SUCH AS SUPERVISED AND UNSUPERVISED LEARNING OF NEURAL NETWORKS, AND THE DIFFERENCE BETWEEN SHALLOW NETWORKS AND DEEP LEARNING NETWORKS. IT ALSO INTENDS TO PRESENT VARIOUS TYPES OF APPLICATIONS IN DIFFERENT AREAS, SUCH AS RESOURCE CONSERVATION, EMISSION REDUCTION, TRAFFIC FLOW MANAGEMENT AND RELATED RISKS, STRENGTHENING THE CIRCULAR ECONOMY, AND NATURAL DISASTER PREVENTION. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING STUDENTS WILL BE ABLE TO APPLY THE ACQUIRED KNOWLEDGE TO A WIDE RANGE OF APPLICATIONS, UTILIZING NEURAL NETWORKS AND ARTIFICIAL INTELLIGENCE FOR VARIOUS TASKS BENEFICIAL TO SUSTAINABILITY. INDEPENDENT JUDGMENT ABILITY TO IDENTIFY THE MOST APPROPRIATE METHODS FOR ANALYZING THE GIVEN PROBLEMS. COMMUNICATION SKILLS ABILITY TO CLEARLY AND CONCISELY DESCRIBE AND ORALLY PRESENT THE OBJECTIVES, PROCEDURES, AND RESULTS OF THE COMPLETED WORK WITH APPROPRIATE LANGUAGE. LEARNING ABILITY ABILITY TO APPLY THE ACQUIRED KNOWLEDGE TO CONTEXTS DIFFERENT FROM THOSE PRESENTED DURING THE COURSE, AND TO DEEPEN THE TOPICS USING MATERIALS OTHER THAN THOSE PROVIDED. |
| Prerequisites | |
|---|---|
| NO PREREQUISITES |
| Contents | |
|---|---|
| INTRODUCTION TO FUNDAMENTAL CONCEPTS IN THE FIELD OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE. SUPERVISED AND UNSUPERVISED LEARNING OF NEURAL NETWORKS. DIFFERENCE BETWEEN SHALLOW NETWORKS AND DEEP LEARNING NETWORKS. THE COURSE ALSO AIMS TO PRESENT VARIOUS TYPES OF APPLICATIONS IN DIFFERENT FIELDS, SUCH AS AGRICULTURE, CATASTROPHIC EVENT RISK MONITORING, RESOURCE CONSERVATION, EMISSION REDUCTION, TRAFFIC FLOW MANAGEMENT AND RELATED RISKS, ENERGY, WATER MANAGEMENT, STRENGTHENING THE CIRCULAR ECONOMY, AND NATURAL DISASTER PREVENTION |
| Teaching Methods | |
|---|---|
| LECTURES AND PRACTICAL EXERCISES. THE LECTURES WILL ENABLE STUDENTS TO ACQUIRE THE NECESSARY THEORETICAL KNOWLEDGE ABOUT MODERN MACHINE LEARNING TECHNIQUES AND WILL ILLUSTRATE THE VARIOUS OPPORTUNITIES (AND CHALLENGES) OF MACHINE LEARNING IN THE FIELD OF SUSTAINABILITY. THE PRACTICAL EXERCISES WILL DEMONSTRATE THE CODE NECESSARY TO IMPLEMENT SIMPLE NEURAL NETWORKS (SUCH AS MULTI-LAYER PERCEPTRON AND SOM) FOR SOME BASIC APPLICATIONS. THE LECTURES WILL ENABLE STUDENTS TO DEVELOP CRITICAL THINKING AND THE ABILITY TO APPLY THE ACQUIRED KNOWLEDGE. |
| Verification of learning | |
|---|---|
| ACHIEVEMENT OF THE COURSE OBJECTIVES IS CERTIFIED BY PASSING THE EXAM WITH A GRADE OUT OF THIRTY. THE EXAM INCLUDES AN ORAL INTERVIEW AIMED AT ASSESSING THE LEVEL OF KNOWLEDGE AND UNDERSTANDING ACHIEVED BY THE STUDENT, AS WELL AS VERIFYING THE ABILITY TO PRESENT USING APPROPRIATE TERMINOLOGY AND THE CAPACITY TO ORGANIZE THE PRESENTATION INDEPENDENTLY. THE ORAL EXAM LASTS ABOUT HALF AN HOUR. HONORS MAY BE AWARDED TO STUDENTS WHO DEMONSTRATE THE ABILITY TO APPLY THE ACQUIRED KNOWLEDGE AND SKILLS CREATIVELY DURING THE ORAL EXAM, SHOWING THE ABILITY TO THINK INDEPENDENTLY. |
| Texts | |
|---|---|
| 1) DEEP LEARNING GOODFELLOW ET AL MIT 2016 2) DEEP LEARNING BISHOP BISHOP 2024 2)MANUALE SULLE RETI NEURALI FLOREANO 3)NEURAL COMPUTING - AN INTRODUCTION BY R. BEALE, TOM JACKSON 4)L’INTELLIGENZA ARTIFICIALE PER LO SVILUPPO SOSTENIBILE HTTPS://WWW.CNR.IT/SITES/DEFAULT/FILES/PUBLIC/MEDIA/ATTIVITA/EDITORIA/VOLUME%20FULL%2014%20DIGITAL%20LIGHT.PDF 5) AI IN THE WILD: SUSTAINABILITY IN THE AGE OF ARTIFICIAL INTELLIGENCE - MIT PRESS REVIEW ARTICLES: - LEVERAGING EDGE ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AGRICULTURE. NATURE SUSTAINABILITY (2024) - MACHINE LEARNING FOR A SUSTAINABLE ENERGY FUTURE , NATURE |
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