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DATA SCIENCE AND APPLICATIONS TO CHEMISTRY

CODICE 104072
ANNO ACCADEMICO 2022/2023
CFU
  • 3 cfu al 2° anno di 9017 SCIENZA E INGEGNERIA DEI MATERIALI (LM-53) - GENOVA
  • 3 cfu al 1° anno di 11430 SCIENZA E TECNOLOGIA DEI MATERIALI (LM SC.MAT.) - GENOVA
  • SETTORE SCIENTIFICO DISCIPLINARE MAT/08
    SEDE
  • GENOVA
  • PERIODO 1° Semestre
    MATERIALE DIDATTICO AULAWEB

    PRESENTAZIONE

    This course aims to introduce the basic computational paradigms of data science and technology, with specific focus on the three pillars of Artificial Intelligence for the data world, i.e. numerical simulation, inverse problems theory and machine learning. Then the course will describe some applications in biochemistry, involving pattern recognition methods for image processing in Scanning Tunnelling Microscopy, the mathematical modelling of tracer kinetics in nuclear medicine and the use of Molecular Interaction Maps in oncology.

    OBIETTIVI E CONTENUTI

    OBIETTIVI FORMATIVI

    The general objective of the course is to provide students with a first overview of the main issues related to modern data science and its cultural background. The course has also two more specific objectives. The first one is to illustrate some computational tools representing the methodological basis for any artificial intelligence approach to data analysis problems. The second one is to describe three applications concerned with the use of data science methods in chemistry and biochemistry: the problem of the automatic recognition and classification of atomic species in Scanning Tunnelling Microscopy; the modelling of glucose metabolism by means of nuclear medicine data; the simulation of the chemical reaction network at the basis of a specific cellular transition in oncogenesis.

    OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

    The general objective of the course is to provide students with a first overview of the main issues related to modern data science and its cultural background. The course has also two more specific objectives. The first one is to illustrate some computational tools representing the methodological basis for any artificial intelligence approach to data analysis problems. The second one is to describe three applications concerned with the use of data science methods in chemistry and biochemistry: the problem of the automatic recognition and classification of atomic species in Scanning Tunnelling Microscopy; the modelling of glucose metabolism by means of nuclear medicine data; the simulation of the chemical reaction network at the basis of a specific cellular transition in oncogenesis.

    PREREQUISITI

    Students attending the course should know in advance the basics of

    • Linear Algebra (vectors, matrices and their norms; linear systems; inversion of a matrix; eigenvalues)

    MODALITA' DIDATTICHE

    oral

    PROGRAMMA/CONTENUTO

    The course is characterized by the following three parts:

    Computational tools: harmonizing competences (8 hours)

    • Basics of numerical analysis (2 hours)
    • Basics of Bayesian theory (3 hours)
    • Basics of regularization theory (3 hours)

    Artificial Intelligence: the many aspects of data modeling (10 hrs)

    • Numerical Simulation (2 hours)
    • Inverse Problems (4 hours)
    • Machine Learning (4 hours)

    Applications to chemical and biochemical data (6 hrs)

    • STM imaging (2 hrs)
    • Tracer kinetics (2 hrs)
    • Chemical Reaction Networks (2 hrs)

    TESTI/BIBLIOGRAFIA

    slides provided by the professor

    DOCENTI E COMMISSIONI

    Commissione d'esame

    MICHELE PIANA (Presidente)

    LEZIONI

    INIZIO LEZIONI

    not known yet

    Orari delle lezioni

    L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.