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