COMPUTAZIONE NATURALE

Ingegneria Informatica COMPUTAZIONE NATURALE

0622700049
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
COMPUTER ENGINEERING
2017/2018



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2016
SECONDO SEMESTRE
CFUHOURSACTIVITY
432LESSONS
18EXERCISES
18LAB
Objectives
THE COURSE INTRODUCES MODELS AND COMPUTATIONAL TECHNIQUES INSPIRED BY NATURE FOR DEVELOPING NEW COMPUTATIONAL METHODS FOR SOLVING COMPLEX PROBLEMS. THE DIFFERENT APPROACHES PROPOSEDIN THE COURSE ARE PRESENTED THROUGH THE COMPARISON OF THEIR PERFORMANCE ON TEST PROBLEMS. THE COURSE INCLUDES AN INTRODUCTION TO THE NEUROPHYSIOLOGY AND EVOLUION OF THE HUMAN BRAIN.
(KNOWLEDGE AND UNDERSTANDING)
KNOWLEDGE OF THE BASICS OF NATURAL EVOLUTION MECHANISMS AND OF PRINCIPLES OF THE NEUROPHYSIOLOGY OF THE HUMAN BRAIN.UNDERSTANDING OF THE COMPUTATIONAL MODEL NATURAL EVOLUTION AND HUMAN BRAIN ACTIVITIES AND THEIR COMPUTATIONAL IMPLEMENTATIONS. KNOWLEDGE OF METHODS AND TECHNIQUES FOR PERFORMANCE EVALUATION. UNDERSTANDING OF THE "BEST PRACTICES" FOR SELECTING THE MOST SUITABLE COMPUTATIONAL MODEL FOR A GIVEN APPLICATION.
(APPLYING KNOWLEDGE AND UNDERSTANDING)
COMPARATIVE PERFORMANCE ANALYSIS OF DIFFERENT COMPUTATIONAL METHODS FOR A GIVEMN APPLICATION. USE OF TEH "BEST PRACTICE" FOR SETTING THE PARAMETERS OF EACH COMPUTATIONAL MODEL.
(MAKING JUDGEMENT)
CHOOSING AND APPLYING THE COMPUTATIONAL MODELS PRESENTED IN THE COURSE FOR PRODUCING HIGH QUALITY SOLUTIONS FOR HIGHLY COMPLEX PROBLEMS. BEING ABLE TO SELECT THE DATA, THE MEASURES AND THE PERFORMANCE INDEX TO RELIABLY ESTIMATE THE PERFORMANCE OF DIFFERENT POSSIBLE SOLUTIONS. COST/BENEFIT ANALYSIS OF THE PROPOSED SOLUTIONS.
(COMMUNICATION SKILLS)
SOCIAL SKILL FOR TEAMWORK, WRITTEN TECHNICAL DPCUMENTATION AND ORAL PRESENTATION OF THE DESIGN ACTIVITY.
Prerequisites
COMPUTER SYSTEM ORGANIZATION, PERFORMANCE MEASURES OF ITS COMPONENTS, ALGORITHMS AND DATA STRUCTURES
Contents
INTRODUCTION (LECTURE: 2H - RECITATION 0H)
THE PARADIGM OF NATURAL COMPUTATION - FUNDAMENTAL CONCEPTS: AGENT, AUTONOMY, INTERACTIVITY, EVALUATION AND FEEDBACK, LEARNING
EVOLUTIONARY COMPUTATION (LECTURES: 6H - RECITATION: 2H)
FOUNDATIONS OF NATURAL EVOLUTION: SELECTION, RICOMBINATION AND MUTATION - THE COMPUTATIONAL METAPHOR - GENETIC ALGORITHMS, EVOLUTIONARU ALGORITHMS AND GENETIC PROGRAMMING
IMMUNE SYSTEMS (LECTURES: 6H - RECITATION: 2H)
FUNDAMENTALS OF IMMUNOLOGY: ANTIGENS AND ANTIBODIES - THE COMPUTATIONAL METAPHOR - ARTIFICIAL IMMUNE SYSTEMS

NEURAL NETWORKS (LECTURES: 6H - RECITATION: 2H)
FOUNDAMENTALS OF NEUROPHYSIOLOGY - THE COMPUTATIONAL METAPHOR - NEURON COMPUTATIONAL MODELS - ARTIFICIAL NEURAL NETWORKS
NEURAL NEWTORKS SYSTEMS (LECTURES: 8H - RECITATION: 2H)
PRINCIPLES OF NEUROSCIENCE - THE COMPUTATIONAL METAPHOR
NEUROCOMPUTATIONAL MODELS - LEVEL OF ABSTRACTION
FINAL PROJECTS (LECTURES: 0H - LABORATORY: 12H)
DESIGN, IMPLEMENTATION AND PERFORMANCE EVALUATION OF THE PROPOSED SOLUTION.
Teaching Methods
THE COURSE INCLUDES LECTURES, CLASSROMM PRACTICE AND LABORATORY ACTIVITIES. DURING CLASSROMM RECITATION, THE MAIN FEATURES OF CONSIDERED MODEL IN DEVELOPING THE FINAL PROJECT ARE PRESENTED AND DISCUSSED. IN THE LAB, THE STUDENTS ARE GROUPED IN TEAMS, AND EACH TEAM MUST DESIGN AND IMPLEMENT A SOLUTION FOR A PROBLEM THE TEAM HAS SELECTED AMONG THOSE PRESENTED DURING RECITATIONS OR PROPOSED BY THE TEAM ITSELF.
Verification of learning
THE FINAL EVALUATION IS CARRIED OUT BY AN ORAL EXAMINATION ON THE TOPICS NOT DIRECTLY RELATED WITH THE FINAL PROJECT AND THE EVALUATION OF THE DESIGN WORK. THE ORAL EXAMINATION AND THE PROJET PRESENTATION AND DISCUSS CONTRIBUTE WITH THE SAME WEIGHT TO THE FINAL SCORE, WHCIH REFERS TO A SCALE FROM 0 TO 30.
Texts
TEXT BOOK
LEANDRO NUNES DE CASTRO - FUNDAMENTALS OF NATURAL COMPUTING,CHAPMAN & HALL/CRC; 1 EDITION, 2006.
ADDITIONAL MATERIAL WILL BE AVAILABLE ON THE COURSE WEBSITE.
ADDITIONAL READING:
DANA H. BALLARD, BRAIN COMPUTATION AS HIERARCHICAL ABSTRACTION, MIT PRESS, 2015
More Information
THE LANGUAGE COURSE IS ITALIAN.
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