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COMPUTATIONAL INTELLIGENCE

CODE 98223
ACADEMIC YEAR 2021/2022
CREDITS
  • 4 cfu during the 1st year of 10728 ENGINEERING TECHNOLOGY FOR STRATEGY (AND SECURITY)(LM/DS) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    Computational Intelligence constitutes a repertoire of Artificial Intelligence predictive methodologies build on data and on domain  knowledge, which are part of the background of the strategic engineer.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Neural networks; fuzzy logic systems; evolutionary computing; swarm intelligence; neuro-fuzzy and fuzzy neural systems; hybrid intelligent systems, machine learning; classification, regression learning, clustering

    AIMS AND LEARNING OUTCOMES

    The course presents a systematic introduction to the foundations and the applications of Computational Intelligence models which are advanced data processing  methods of Artificial Intelligence inspired by natural systems and that encompass artificial neural networks, fuzzy logic systems, evolutionary calculus, swarm intelligence and machine learning. The most relevant topics, such as classification and regression, will be addressed both from a theoretical point of view and through practical programming exercises and homework using the Python language.

    PREREQUISITES

    The Course does not require specific prerequisites and includes all the necessary elements and references. The basic knowledge in mathematics, statistics acquired in previous studies, and programming skills in Python will be useful for improving the learning curve and student performance. 

     

    TEACHING METHODS

    1 Lecture of 4 hours in a row per week for 10 weeks including frontal lectures, Class exercises and home-works.


     

    SYLLABUS/CONTENT

    Optimization; Machine Learning; Regression; Classification; Bayesian Decision Theory; Parametric Classification; Intro to clustering; Fuzzy Sets; Fuzzy Clustering;  Kernel Clustering; Spectral Clustering; Networks' Analysis; Neural Networks; Support Vector Machines;  Multi-Layer Perceptrons; Fuzzy Systems; Deep Learning;  Ensembles; Genetic Algorithms;  Evolution Strategies; Particle Swarm Optimization; Multi-Objective Genetic Algorithms; Multimodal Medical Volumes Segmentation; Seminars by companies operating in AI; Demos; Homeworks.

     

    RECOMMENDED READING/BIBLIOGRAPHY

    • Textbook: Andries P. Engelbrecht: Computational Intelligence - An introduction, Wiley, 2007.

    • Selection of relevant journal papers

    • Lecture notes / slides 


     

    TEACHERS AND EXAM BOARD

    Exam Board

    FRANCESCO MASULLI (President)

    AGOSTINO BRUZZONE

    ALBERTO CABRI

    STEFANO ROVETTA (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Homeworks and oral exam

    Exam schedule

    Date Time Location Type Notes
    12/01/2022 10:00 GENOVA Orale
    26/01/2022 10:00 GENOVA Orale
    15/02/2022 10:00 GENOVA Orale
    06/06/2022 10:00 GENOVA Orale
    14/07/2022 10:00 GENOVA Orale
    27/07/2022 10:00 GENOVA Orale
    14/09/2022 10:00 GENOVA Orale