Genoveffa TORTORA | ARTIFICIAL INTELLIGENCE
Genoveffa TORTORA ARTIFICIAL INTELLIGENCE
cod. 0522700006
ARTIFICIAL INTELLIGENCE
| 0522700006 | |
| COMPUTER SCIENCE | |
| EQF7 | |
| CYBERSECURITY AND CLOUD TECHNOLOGIES | |
| 2023/2024 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2023 | |
| AUTUMN SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 4 | 32 | LESSONS | |
| INF/01 | 2 | 16 | LAB |
| Objectives | |
|---|---|
| THE GOAL OF THIS COURSE IS TO PROVIDE STUDENTS WITH THEORETICAL FOUNDATIONS OF INTELLIGENT AGENTS AND THEIR INTERACTION WITH THE ENVIRONMENT, AS WELL AS THE MAIN TECHNIQUES OF KNOWLEDGE REPRESENTATION AND SEMANTIC INFERENCE, FOR DEVELOPING INTELLIGENT SYSTEMS KNOWLEDGE AND UNDERSTANDING - KNOWLEDGE OF THE FOUNDATIONS OF ARTIFICIAL INTELLIGENCE; - UNDERSTANDING WHAT KNOWLEDGE REPRESENTATION, INFERENCE, AND MACHINE LEARNING TECHNIQUES ARE THE MOST APPROPRIATE IN DIFFERENT REAL-WORLD APPLICATIONS; - UNDERSTANDING THE ETHICAL AND EXPLAINABILITY ISSUES RELATED TO THE USE OF ARTIFICIAL INTELLIGENCE IN APPLICATION DOMAINS. APPLYING KNOWLEDGE AND UNDERSTANDING: - DESIGNING AND IMPLEMENTING AN INTELLIGENT SYSTEM, KNOWING HOW TO IDENTIFY THE MOST APPROPRIATE KNOWLEDGE REPRESENTATION, INFERENCE, AND/OR MACHINE LEARNING TECHNIQUES; - USING FORMAL PROCEDURES FOR THE EVALUATION AND ANALYSIS OF ACHIEVED RESULTS AND THEIR GENERALIZATION TO REAL-WORLD APPLICATIONS. AUTONOMY OF JUDGMENT - AUTONOMOUSLY DEVELOPING ARTIFICIAL INTELLIGENCE ALGORITHMS AND CRITICALLY ANALYZING OBTAINED RESULTS; -INDEPENDENTLY INVESTIGATING OTHER LITERATURE BY MASTERING LEARNED FUNDAMENTAL CONCEPTS. COMMUNICATION SKILLS -ACQUIRING ADEQUATE EXPRESSIVENESS SKILLS IN COMMUNICATING ISSUES CONCERNING INTELLIGENT SYSTEMS AND THEIR APPLICATIONS. LEARNING SKILLS -ADAPTING ACQUIRED KNOWLEDGE TO NEW CONTEXTS AND UNDERSTANDING THE LIMITS OF APPLICABILITY OF ARTIFICIAL INTELLIGENCE TECHNIQUES. |
| Prerequisites | |
|---|---|
| STUDENTS SHOULD BE FAMILIAR WITH PROBABILITY AND STATISTICS, LINEAR ALGEBRA, PROGRAMMING, AND MACHINE LEARNING METHODS. NO PROPAEDEUTIC COURSE IS REQUIRED. |
| Contents | |
|---|---|
| AFTER INTRODUCING THE ARTIFICIAL INTELLIGENCE FROM BOTH HISTORICAL AND APPLICATION PERSPECTIVES THE COURSE WILL FOCUS ON THE FOLLOWING TOPICS: INTELLIGENT AGENTS (2 HOURS) PROBLEM-SOLVING - SOLVING PROBLEMS BY SEARCHING (1 HOUR) - CONSTRAINT SATISFACTION PROBLEMS (1 HOUR) - ADVERSARIAL SEARCH AND GAMES (2 HOURS) KNOWLEDGE, REASONING, AND PLANNING - LOGICAL AGENTS (1 HOUR) - FIRST-ORDER LOGIC (2 HOURS) - INFERENCE IN FIRST-ORDER LOGIC (1 HOUR) KNOWLEDGE REPRESENTATION - SEMANTIC NETWORKS, DESCRIPTION LOGICS, FOUNDATION OF ONTOLOGIES (2 HOURS) - REPRESENTING ACTIONS, SITUATIONS, AND EVENTS (1 HOUR) - NONMONOTONIC REASONING AND REASONING WITH DEFAULT INFORMATION (1 HOUR) - TRUTH MAINTENANCE SYSTEMS (2 HOURS) UNCERTAIN KNOWLEDGE AND REASONING - PROBABILISTIC REASONING (1 HOUR) - MAKING DECISIONS (1 HOUR) - MULTIAGENT DECISION-MAKING (1 HOUR) - PROBABILISTIC PROGRAMMING (2 HOURS) MACHINE LEARNING - LEARNING PROBABILISTIC MODELS (2 HOURS) - DEEP LEARNING (2 HOURS) - REINFORCEMENT LEARNING (2 HOURS) COMMUNICATING, PERCEIVING, AND ACTING - NATURAL LANGUAGE PROCESSING (1 HOUR) - ROBOTICS (1 HOUR) - COMPUTER VISION (1 HOUR) ETHICS - PHILOSOPHY & ETHICS (1 HOUR) - EXPLAINABILITY & SAFETY OF AI (1 HOUR) LABORATORY - INTRODUCTION TO PYTHON LANGUAGE (2 HOURS) - IMPLEMENTING INTELLIGENT AGENTS & GAMES IN PYTHON (4 HOURS) - INTRODUCTION TO PROLOG AND ITS EXTENSIONS (4 HOURS) - MACHINE LEARNING WITH PYTHON (4 HOURS) - EXAMPLES OF APPLICATIONS (2 HOURS) |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES: - FRONTAL LECTURES TO TRANSFER THE KNOWLEDGE RELATED TO THE COURSE CONTENTS (4 CFUS/32 HOURS) - LABORATORY SESSIONS AND TUTORIALS TO TRAIN STUDENTS ON PRACTICAL AND COLLABORATIVE ACTIVITIES (2 CFUS/16 HOURS) - EACH LECTURE WILL INCLUDE BOTH THE PRESENTATION BY TEACHERS OF THE COURSE CONTENTS AND TUTORIALS ON THEIR PRACTICAL APPLICATION |
| Verification of learning | |
|---|---|
| THE EXAM CONSISTS OF A PRELIMINARY WRITTEN TEST AND AN ORAL EXAMINATION TO VERIFY THE ACQUIRED KNOWLEDGE AND TO DISCUSS THE ACTIVITIES CARRIED OUT DURING THE COURSE. ACTIVITIES INCLUDE THE REALIZATION OF A PROJECT IN A GROUP. WRITTEN EXAMS CAN BE REPLACED BY PROGRESSIVE ASSESSMENT TESTS THAT INCLUDE QUESTIONS CONCERNING BOTH THE KNOWLEDGE AND UNDERSTANDING OF CLASSROOM ARGUMENTS AND THE ABILITY TO APPLY THEM THROUGH EXERCISES. WRITTEN EXAMINATION (2 HOURS) TO EVALUATE THE GAINED KNOWLEDGE ON ARTIFICIAL INTELLIGENCE TECHNIQUES, THE TESTS WILL BE COMPOSED OF OPEN QUESTIONS AND EXERCISES. THE SCORES ARE ASSIGNED DEPENDING ON THE COMPLEXITY OF THE QUESTIONS OR EXERCISES (BETWEEN 4 AND 10 POINTS). THE EVALUATION CRITERIA INCLUDE THE CORRECTNESS AND COMPLETENESS OF THE LEARNING AND THE CLARITY OF THE PRESENTATION. THE FINAL MARK IS OUT OF 30. ASSESSMENT TESTS NON-CUMULATIVE TESTS COULD BE DELIVERED. STUDENTS WHO WILL PASS THE TESTS WILL BE EXEMPTED FROM THE WRITTEN EXAMINATION. THE AIM IS TO ENCOURAGE STUDENTS TO FOLLOW EFFECTIVELY THE COURSE. PROJECT THE PROJECT ALLOWS THE STUDENT TO PRACTICE ON THE CONTENTS LEARNED DURING THE COURSE. DURING THE ORAL EXAM, THE PROJECT WILL BE DISCUSSED DIRECTLY WITH THE TEACHER THAT WILL VERIFY THE FOLLOWING: - COMPLETENESS AND THE CORRECTNESS OF THE PROJECT - COMPREHENSION OF THE REALIZED ARTIFACTS - LEVEL OF FAMILIARITY AND ABILITY TO MODIFY THE PRODUCED SOFTWARE. ORAL EXAMINATION TO EVALUATE THE GENERAL KNOWLEDGE OF THE STUDENT WITH RESPECT TO THE ENTIRE COURSE PROGRAM. THE EVALUATION CRITERIA INCLUDE THE COMPLETENESS AND CORRECTNESS OF THE LEARNING AND THE CLARITY OF THE PRESENTATION. FINAL EVALUATION THE EVALUATION WILL BE GIVEN BY THE AVERAGE SCORE OF ASSESSMENT TESTS (OR THE WRITTEN EXAMINATION) AND THE POINTS OBTAINED BY DISCUSSING THE PROJECT AND THE ORAL TEST. |
| Texts | |
|---|---|
| COURSE BOOK: S.J. RUSSELL, P. NORVIG, “ARTIFICIAL INTELLIGENCE, A MODERN APPROACH”, 4TH EDITION, PEARSON EDUCATION, 2020. RECOMMENDED READING: - R. J. BRACHMAN, H. J. LEVESQUE, “KNOWLEDGE REPRESENTATION AND REASONING”, ELSEVIER, 2004. - F. BAADER, D. CALVANESE, D.L. MCGUINNESS, D. NARDI, P.F. PATEL-SCHNEIDER (EDITORS), “THE DESCRIPTION LOGIC HANDBOOK: THEORY, IMPLEMENTATION, AND APPLICATIONS”, CAMBRIDGE UNIVERSITY PRESS NEW YORK, NY, USA, 2007 - P. DERANSART, A. ED-DBALI, L. CERVONI. “PROLOG: THE STANDARD: REFERENCE MANUAL”, SPRINGER SCIENCE & BUSINESS MEDIA, 1996. - AURÉLIEN GÉRON, “HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN, KERAS, AND TENSORFLOW: CONCEPTS, TOOLS, AND TECHNIQUES TO BUILD INTELLIGENT SYSTEMS”,2ND EDITION, O REILLY ED, 2019. |
| More Information | |
|---|---|
| ATTENDANCE OF LECTURES IS STRONGLY ENCOURAGED. STUDENTS MUST SPEND A CONSIDERABLE AMOUNT OF TIME STUDYING AT HOME, AND FOR DEVELOPING THE COURSE PROJECT. INFORMATION CONCERNING THE COURSE IS AVAILABLE ON THE E-LEARNING PLATFORM OF THE DIPARTIMENTO DI INFORMATICA AT HTTP://ELEARNING.INFORMATICA.UNISA.IT/EL-PLATFORM/ CONTACTS PROF. GENOVEFFA TORTORA TORTORA@UNISA.IT PROF. LOREDANA CARUCCIO LCARUCCIO@UNISA.IT |
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