Vincenzo DEUFEMIA | Artificial Intelligence
Vincenzo DEUFEMIA Artificial Intelligence
cod. 0522500093
ARTIFICIAL INTELLIGENCE
| 0522500093 | |
| DIPARTIMENTO DI INFORMATICA | |
| EQF7 | |
| COMPUTER SCIENCE | |
| 2016/2017 |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| PRIMO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 9 | 72 | LESSONS |
| Objectives | |
|---|---|
| KNOWLEDGE AND UNDERSTANDING THE STUDENT WILL ACQUIRE BASIC SKILLS ON •INTELLIGENT AGENTS, •SEARCH STRATEGIES FOR PROBLEM SOLVING, •KNOWLEDGE REPRESENTATION AND INFERENCE, WITH PARTICULAR EMPHASIS ON THOSE BASED ON FIRST ORDER LOGIC AND RELATED INFERENCE METHODS, •REPRESENTATION OF UNCERTAIN KNOWLEDGE AND PROBABILISTIC INFERENCE METHODS, WITH SPECIAL EMPHASIS ON BAYESIAN NETWORKS, •MACHINE LEARNING TECHNIQUES. APPLYING KNOWLEDGE AND UNDERSTANDING THE STUDENT WILL ACQUIRE THE FOLLOWING APPLICATIVE SKILLS: - ANALYSIS OF PRACTICAL ARTIFICIAL INTELLIGENCE PROBLEMS; - DESIGN OF INTELLIGENT SYSTEMS TO SOLVE REAL PROBLEMS. |
| Prerequisites | |
|---|---|
| BACHELOR LEVEL FOUNDAMENTALS OF STATISTICS AND PROBABILITY, PROGRAMMING, DATABASE, AND OPERATIONAL RESEARCH. |
| Contents | |
|---|---|
| PART I: INTRODUCTION TO ARTIFICIAL INTELLIGENCE INTELLIGENT AGENTS PART II: PROBLEM SOLVING SOLVING PROBLEMS BY SEARCHING BEYOND CLASSICAL SEARCH ADVERSARIAL SEARCH PART III: KNOWLEDGE AND REASONING LOGICAL AGENTS FIRST-ORDER LOGIC INFERENCE IN FIRST-ORDER LOGIC KNOWLEDGE REPRESENTATION PART IV: UNCERTAIN KNOWLEDGE AND REASONING QUANTIFYING UNCERTAINTY PROBABILISTIC REASONING PART V: LEARNING LEARNING FROM EXAMPLES KNOWLEDGE IN LEARNING NEURAL NETWORKS KERNEL MACHINES |
| Teaching Methods | |
|---|---|
| LECTURES AND EXERCISES TO INTRODUCE CONCEPTS AND TO DEVELOP PROBLEM SOLVING CAPABILITIES USING AI TECHNIQUES AND TOOLS. |
| Verification of learning | |
|---|---|
| STUDENTS MUST DEVELOP A PROJECT ON A COURSE TOPIC THEY CHOOSE. MOREOVER, THEY MUST UNDERGO A WRITTEN EXAMINATION, WHICH ASSESSES THE CAPABILITY OF SOLVING EXERCISES BY MEANS OF THE TECHNIQUES INTRODUCED DURING THE COURSE, AND AN ORAL INTERVIEW, WHICH ASSESSES THE LEVEL OF UNDERSTANDING OF THE COURSE TOPICS AND THE ABILITY TO EXPRESS THEM CLEARLY. |
| Texts | |
|---|---|
| S.J.RUSSELL, P. NORVIG, ARTIFICIAL INTELLIGENCE, A MODERN APPROACH, (3D/ED, 2009), PEARSON EDUCATION. R.A. ELMASRI, S.B. NAVATHE, “FUNDAMENTALS OF DATABASE SYSTEMS”, 7TH ED., ADDISON WESLEY, 2016. OTHER BOOKS C.M. BISHOP: “PATTERN RECOGNITION AND MACHINE LEARNING”, SPRINGER SCIENCE, NEW YORK, 2006. GOLFARELLI M., RIZZI S., “DATA WAREHOUSE - TEORIA E PRATICA DELLA PROGETTAZIONE”, THE MCGRAW-HILL COMPANIES, 2006. |
| More Information | |
|---|---|
| COURSE ATTENDANCE IS STRONGLY RECOMMENDED. STUDENTS MUST BE PREPARED TO SPEND A FAIR AMOUNT OF TIME IN THE STUDY OUTSIDE OF LESSONS. FOR A SATISFACTORY PREPARATION STUDENTS NEED TO SPEND AN AVERAGE OF TWO HOURS OF STUDY TIME FOR EACH HOUR SPENT IN CLASS. HALF OF THIS TIME IS NECESSARY TO ACQUIRE AN ADEQUATE FAMILIARITY WITH SOFTWARE TOOLS FOR DEVELOPING INTELLIGENT SYSTEMS. COURSE MATERIALS WILL BE AVAILABLE FOR DOWNLOAD FROM THE DEPARTMENTAL E-LEARNING PLATFORM HTTP://ELEARNING.INFORMATICA.UNISA.IT/EL-PLATFORM/ CONTACTS PROF. VINCENZO DEUFEMIA DEUFEMIA@UNISA.IT PROF. GIUSEPPE POLESE GPOLESE@UNISA.IT |
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