CODE  104072 

ACADEMIC YEAR  2022/2023 
CREDITS 

SCIENTIFIC DISCIPLINARY SECTOR  MAT/08 
TEACHING LOCATION 

SEMESTER  1° Semester 
TEACHING MATERIALS  AULAWEB 
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.
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.
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.
Students attending the course should know in advance the basics of
lectures and computational laboratory activity
The course is characterized by the following three parts:
Computational tools: harmonizing competences (8 hours)
Artificial Intelligence: the many aspects of data modeling (10 hrs)
Applications to chemical and biochemical data (6 hrs)
no bibliography
Office hours: Office hours by appointment via email
MICHELE PIANA (President)
not known yet
All class schedules are posted on the EasyAcademy portal.
oral
questions about the course syllabus