CODE 104072 ACADEMIC YEAR 2025/2026 CREDITS 3 cfu anno 2 SCIENZA E TECNOLOGIA DEI MATERIALI 11430 (LM SC.MAT.) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/08 LANGUAGE English 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 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. 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 Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita 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 slides provided by the professor TEACHERS AND EXAM BOARD MICHELE PIANA Ricevimento: By appointment via e-mail (michele.piana@unige.it) LESSONS LESSONS START TBD Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION oral ASSESSMENT METHODS questions about the course syllabus FURTHER INFORMATION Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison. Agenda 2030 - Sustainable Development Goals Good health and well being Affordable and clean energy Life on land