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DATA SCIENCE AND APPLICATIONS TO CHEMISTRY

CODE 104072
ACADEMIC YEAR 2021/2022
CREDITS
  • 3 cfu during the 2nd year of 9017 SCIENZA E INGEGNERIA DEI MATERIALI (LM-53) - GENOVA
  • 3 cfu during the 1st year of 9017 SCIENZA E INGEGNERIA DEI MATERIALI (LM-53) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR MAT/08
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    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.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    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.

    AIMS AND LEARNING OUTCOMES

    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.

    PREREQUISITES

    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)

    TEACHING METHODS

    lectures and computational laboratory activity

    SYLLABUS/CONTENT

    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)

    RECOMMENDED READING/BIBLIOGRAPHY

    no bibliography

    TEACHERS AND EXAM BOARD

    Exam Board

    MICHELE PIANA (President)

    LESSONS

    LESSONS START

    not known yet

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    oral

    ASSESSMENT METHODS

    questions about the course syllabus