Fabio Narducci | FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE
Fabio Narducci FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE
cod. 0512100055
FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE
| 0512100055 | |
| DIPARTIMENTO DI INFORMATICA | |
| EQF6 | |
| COMPUTER SCIENCE | |
| 2020/2021 |
| YEAR OF COURSE 3 | |
| YEAR OF DIDACTIC SYSTEM 2017 | |
| PRIMO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 6 | 48 | LESSONS |
| Objectives | |
|---|---|
| THE COURSE IS MEANT TO INTRODUCE PROBLEM SOLVING PRINCIPLES AND TECHNIQUES ADOPTED IN THE FIELD OF ARTIFICIAL INTELLIGENCE (WITH SPECIAL FOCUS ON KNOWLEDGE BASED SYSTEMS AND LOGIC BASED METHODS). KNOWLEDGE AND UNDERSTANDING STUDENTS ARE EXPECTED TO GAIN KNOWLEDGE AND GOOD AWARENESS OF PROBLEM SOLVING CONCEPTS AND METHODS IN ARTIFICIAL INTELLIGENCE. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING STUDENT IS EXPECTED TO BE ABLE TO DEFINE AND DEVELOP INFERENCE ENGINES THROUGH IMPERATIVE OR DECLARATIVE PROGRAMMING LANGUAGES THE STUDENT WILL BE ABLE TO DEFINE AND IMPLEMENT SOLVERS FOR KNOWLEDGE-BASED SYSTEMS THROUGH IMPERATIVE OR DECLARATIVE LANGUAGES: - ABILITY TO RECOGNIZE PROBLEMS SOLVABLE THROUGH THE ADOPTION OF ARTIFICIAL INTELLIGENCE ALGORITHMS; - ABILITY TO IDENTIFY WHICH IS THE MOST SUITABLE SOLUTION TO THE RESOLUTION OF AN ARTIFICIAL INTELLIGENCE PROBLEM AMONG THE VARIOUS POSSIBLE ALTERNATIVES. - ABILITY TO MODEL AND SOLVE AN ARTIFICIAL INTELLIGENCE PROBLEM; - ABILITY TO IMPLEMENT A SOLUTION TO AN ARTIFICIAL INTELLIGENCE PROBLEM THROUGH THE USE OF METHODOLOGIES AND TOOLS AVAILABLE ON THE MARKET. |
| Prerequisites | |
|---|---|
| THE STUDENT MUST HAVE ACQUIRED BASIC KNOWLEDGE AND SKILLS ON MATHEMATICAL LOGIC, PROBABILITY, AND PROGRAMMING. |
| Contents | |
|---|---|
| THE STUDENT WILL ACQUIRE KNOWLEDGE AND SKILLS ON THE MAIN CONCEPTS AND METHODS REPRESENTING THE GROUND FOR THE RESOLUTION OF ARTIFICIAL INTELLIGENCE PROBLEMS. SPECIFICALLY: - MODELING AND REPRESENTATION OF ARTIFICIAL INTELLIGENCE PROBLEMS; - SMART AGENTS; - SEARCH-BASED ALGORITHMS; - ADVERSARIAL SEARCH-BASED ALGORITHMS; - HEURISTIC AND META-HEURISTIC ALGORITHMS; - UNSUPERVISED LEARNING ALGORITHMS; - SUPERVISED LEARNING ALGORITHMS; - METHODS OF EVALUATION OF ARTIFICIAL INTELLIGENCE ALGORITHMS. THE COURSE WILL PROVIDE BASIC KNOWLEDGE ON THE FOLLOWING TOPICS: KNOWLEDGE REPRESENTATION: - BACKGROUND ON COGNITIVE PSYCHOLOGY, ON THE BORN AND RISE OF ARTIFICIAL INTELLIGENCE, AND ON THE RELATION BETWEEN COGNITIVE PSYCHOLOGY AND ARTIFICIAL INTELLIGENCE; - SMART AGENTS AND KNOWLEDGE REPRESENTATION STARTING FROM A TEXTUAL DESCRIPTION. BAYESIAN DECISION THEORY: - INTRODUCTION TO PROBABILITY; - THE BAYESIAN DECISION; - THE BAYESIAN RISK; - NORMAL DENSITIES AND BAYESIAN CLASSIFICATION. RESOLUTION OF PROBLEMS THROUGH SEARCH-BASED ALGORITHMS: - INFORMED SEARCH TECHNIQUES; - UNINFORMED SEARCH TECHNIQUES; - ADVERSARIAL SEARCH TECHNIQUES; - HEURISTIC SEARCH TECHNIQUES; - META-HEURISTIC SEARCH TECHNIQUES. LEARNING TECHNIQUES: - EXTRACTION AND REDUCTION OF DATA CHARACTERISTICS; - SUPERVISED LEARNING ALGORITHMS, LIKE MACHINE LEARNING ALGORITHMS THAT INCLUDE CLASSIFICATION TECHNIQUES, VALIDATION, AND QUALITY OF DATA. - UNSUPERVISED LEARNING ALGORITHMS, LIKE CLUSTERING ALGORITHMS AND ASSOCIATION RULE MINING. - PERFORMANCE ANALYSIS (ACCURACY VS PRECISION, CONFUSION MATRICES, ROC-CURVE, CMC-CURVE, ETC.). APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN REAL CONTEXTS: TO THIS AIM, THE STUDENT WILL ACQUIRE KNOWLEDGE ON DIFFERENT TOOLS, LIKE R, PYTHON SKLEARN, WEKA AND OTHERS. |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES 48 HOURS OF FRONTAL LECTURES AND EXERCISES (6 ECTS), WITH THE GOAL OF PRESENTING THE ENVISIONED CONCEPTS AND DEVELOPING THE CAPABILITIES NEEDED FOR THE RESOLUTION OF ARTIFICIAL INTELLIGENCE PROBLEMS THROUGH THE USE OF THE (SEMI-)AUTOMATIC TOOLS DISCUSSED IN THE CONTEXT OF THE COURSE. |
| Verification of learning | |
|---|---|
| THE VERIFICATION OF THE SKILLS ACQUIRED BY THE STUDENT WILL BE VERIFIED THROUGH AN EXAM, WITH EVALUATION IN THIRTIETHS. THE EXAM INCLUDES A WRITTEN EXAM AS WELL AS THE DEVELOPMENT OF A PROJECT. - THE WRITTEN EXAM HAS THE GOAL OF VERIFYING THE THEORETICAL SKILLS ACQUIRED BY THE STUDENT ON THE USAGE OF ARTIFICIAL INTELLIGENCE METHODOLOGIES AND TECHNIQUES; - THE PROJECT HAS THE GOAL OF EVALUATING THE COMPLETENESS AND CORRECTNESS OF A PROJECT RELATED TO THE APPLICATION OF ARTIFICIAL INTELLIGENCE METHODOLOGIES AND TECHNIQUES IN REAL CONTEXTS. FURTHERMORE, IT HAS THE GOAL OF ASSESSING THE LANGUAGE SKILLS AS WELL AS THE ABILITY TO PROPERLY MOTIVATE THE CHOICES DONE DURING THE PROJECT DEVELOPMENT. AT THE END OF THE PROJECT, THE STUDENT WILL DELIVER (1) A REPORT CONTAINING THE PROJECT DOCUMENTATION AND (2) A 15-MINUTE PRESENTATION - DEVELOPED USING KEYNOTE, POWERPOINT, OR GOOGLE PRESENTATION). THE FINAL EVALUATION WILL TAKE THE OUTCOME OF THE TWO PARTS INTO ACCOUNT. |
| Texts | |
|---|---|
| S. J. RUSSELL, P. NORVIG. “ARTIFICIAL INTELLIGENCE: A MODERN APPROACH”, PRENTICE HALL, 2002. FURTHER RECOMMENDED READINGS: 1. C. M. BISHOP. “PATTERN RECOGNITION AND MACHINE LEARNING”, SPRINGER SCIENCE, NEW YORK, 2006. 2. DUDA, R. O., HART, P. E., & STORK, D. G. (2012). PATTERN CLASSIFICATION. JOHN WILEY & SONS. |
| More Information | |
|---|---|
| ATTENDING THE COURSE IS NOT MANDATORY BUT STRONGLY RECOMMENDED. STUDENTS MUST BE READY TO ATTEND THE COURSE ACTIVELY, THROUGH THE INTERACTION WITH THE LECTURERS AS WELL AS THE INDIVIDUAL STUDY OF THE MATERIAL TAUGHT DURING THE LECTURES. A SATISFACTORY PREPARATION WHICH LEADS TO PASSING THE EXAM WILL CONSIST OF AN AVERAGE INDIVIDUAL STUDY OF TWO HOURS FOR EACH HOUR OF LECTURE AND AN AVERAGE OF ONE HOUR DEVOTED TO THE DEVELOPMENT OF THE PROJECT. BY DESIGN, THE COURSE EXPECTS A STRONG PREDISPOSITION TO LEARNING NEW SOFTWARE INSTRUMENTS FOR THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE MODULES. THE DIDACTIC MATERIAL WILL BE MADE AVAILABLE ON THE E-LEARNING PLATFORM OF THE DEPARTMENT |
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