NATURAL COMPUTATION

International Teaching NATURAL COMPUTATION

0622900013
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
DIGITAL HEALTH AND BIOINFORMATIC ENGINEERING
2021/2022



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2018
SECONDO SEMESTRE
CFUHOURSACTIVITY
324LESSONS
216EXERCISES
18LAB
Objectives
MODELS AND COMPUTATIONAL TECHNIQUES INSPIRED BY NATURE FOR SOLVING COMPLEX PROBLEMS AND OF STRENGTHS/WEAKNESSES BETWEEN THE DIFFERENT APPROACHES DISCUSSED IN THE LECTURES.

KNOWLEDGE AND UNDERSTANDING
BASIC OF THE MECHANISMS AND THE PRINCIPLES OF THE DARWINIAN EVOLUTION, THE IMMUNE SYSTEM, THE SWARM INTELLIGENCE AND THE NEUROPHYSIOLOGY OF THE HUMAN BRAIN.COMPUTATIONAL MODELS AND THEIR IMPLEMENTATIONS. METHODS AND TECHNIQUES FOR PERFORMANCE EVALUATION. "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 GIVEN APPLICATION. USE OF THE "BEST PRACTICE" FOR SOLVING OPTIMIZATION AND MACHINE LEARNING PROBLEMS.
Prerequisites
COMPUTER SYSTEM ORGANIZATION, PERFORMANCE MEASURES OF ITS COMPONENTS, ALGORITHMS AND DATA STRUCTURES
Contents
INTRODUCTION (LECTURE / PRACTICE / LABORATORY HOURS 2/0/0)
THE PARADIGM OF NATURAL COMPUTATION - FUNDAMENTAL CONCEPTS: AGENT, AUTONOMY, INTERACTIVITY, EVALUATION AND FEEDBACK, LEARNING

EVOLUTIONARY COMPUTATION (LECTURE / PRACTICE / LABORATORY HOURS 6/2/0)
FOUNDATIONS OF NATURAL EVOLUTION: SELECTION, RICOMBINATION AND MUTATION - THE COMPUTATIONAL METAPHOR - GENETIC ALGORITHMS, EVOLUTIONARU ALGORITHMS AND GENETIC PROGRAMMING

IMMUNE SYSTEMS (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0)
FUNDAMENTALS OF IMMUNOLOGY: ANTIGENS AND ANTIBODIES - THE COMPUTATIONAL METAPHOR - ARTIFICIAL IMMUNE SYSTEMS

NEURAL NETWORKS (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0)
FOUNDAMENTALS OF NEUROPHYSIOLOGY - THE COMPUTATIONAL METAPHOR - NEURON COMPUTATIONAL MODELS - ARTIFICIAL NEURAL NETWORKS

SWARM INTELLIGENCE (LECTURE / PRACTICE / LABORATORY HOURS 6/2/0)
COLONIE DI FORMICHE: RICERCA DEL CIBO E RIMOZIONE DEI CADAVERI - ALGORITMI DI OTTIMIZZAZIONE E CLUSTERING - SCIAMI DI PARTICELLE: ALGORITMO PSO

COMPUTATIONAL NEUROSCIENCE (LECTURE / PRACTICE / LABORATORY HOURS 6/2/0)
PRINCIPLES OF NEUROSCIENCE - THE COMPUTATIONAL METAPHOR
NEUROCOMPUTATIONAL MODELS - LEVEL OF ABSTRACTION

FINAL PROJECT (LECTURE / PRACTICE / LABORATORY HOURS 0/0/10)
PRESENTATION OF THE DESIGN ASSIGMENT AND RELATED TOOLS- STUDENT TEAM WORKING SUPERVISION

TOTAL LECTURE / PRACTICE / LABORATORY HOURS 28/10/10
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 PRESENTAZION OF THE DESIGN WORK. THE FINAL GRADE IS THE WEIGHTED SUM OF THE DESIGN (40%), ITS PRESENTATION (20%) AND THE ORAL EXAMINATION.
Texts
L. NUNES DE CASTRO - FUNDAMENTALS OF NATURAL COMPUTING,CHAPMAN & HALL/CRC; 1 EDITION, 2006.
A. BRABAZON, M. O'NEILL AND S. MCGARRAGHY, NATURAL COMPUTING ALGORITHMS, SPRINGER, 2015

SUPPLEMENTARY TEACHING MATERIAL WILL BE AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.

ADDITIONAL READING:
DANA H. BALLARD, BRAIN COMPUTATION AS HIERARCHICAL ABSTRACTION, MIT PRESS, 2015"
More Information
THE COURSE IS HELD IN ENGLISH.
  BETA VERSION Data source ESSE3