Course catalogue
Create your own master’s programme by choosing between the different specializations of our partner universities.
Master SERP+ Programme - cohort 2020-2025
Data Science and Applications to Chemistry (3 ECTS)
All courses during this semester
All courses during this semester
- Transferable skills: Polish course, Summer School in Entrepreneurship (6 ECTS)
- The molecules of life: from structure to chemical function (3 ECTS)
- Selected in silico and in vitro methods in thermodynamics and soft matter (6 ECTS)
- Organic chemistry (3 ECTS)
- Introduction to solid state (6 ECTS)
- Dynamics of photochemical reactions in chemistry, biology and medicine (6 ECTS)
- Transferable skills: Portuguese course, Summer School in Entrepreneurship (6 ECTS)
- Solid State Physics (6 ECTS)
- Molecular Energetics (3 ECTS)
- Laboratory of Materials and Surface Analysis (6 ECTS)
- Interfacial Electrochemistry (3 ECTS)
- Interfaces, Colloids and Self-Assembly (6 ECTS)
- Transferable skills: Summer School in Entrepreneurship (3 ECTS)
- Organic Photochemistry (3 ECTS)
- Italian Courses (3 ECTS)
- Introduction to Solid State (6 ECTS)
- Inorganic Functional Materials (3 ECTS)
- Electrochemical systems for energy conversion and storage (6 ECTS)
- Chemistry and Technology of Catalysis and Laboratory (6 ECTS)
All courses during this semester
- Transferable skills: Scientific writing, French courses - 5ECTS
- Nanosciences (6 ECTS)
- Nanoparticles and Advanced radiation therapies (6 ECTS)
- Fundamentals in data science and machine learning (3 ECTS)
- Femtochemistry (3 ECTS)
- Chemistry for renewable energy: from advanced research to industrial applications (6 ECTS)
- Transferable skills: Scientific writing, Polish courses (6 ECTS)
- Lanthanide luminescence: Application in chemistry and biology (6 ECTS)
- Introduction to Data Sciences (3 ECTS)
- Environmental photochemistry (3 ECTS)
- Computational and quantum photochemistry (6 ECTS)
- Applied photochemistry and luminescence spectroscopy (6 ECTS)
- Scientific Writing and Career Objectives (3 ECTS)
- Portuguese course (3 ECTS)
- Nanotechnologies, Micro and Nano-fabrication (6 ECTS)
- Materials Properties and Applications (6 ECTS)
- Electrochemical Technology (6 ECTS)
- Data Science Basics (3 ECTS)
- Bionanotechnology (3 ECTS)
- Transferable skills: Scientific Writing Industrial Seminars (3 ECTS)
- Surface Science and Nanostructuring at Surfaces (6 ECTS)
- Polymers for electronics and energy harvesting (6 ECTS)
- Laboratory on device building (3 ECTS)
- Italian Courses (3 ECTS)
- Data Science and Applications to Chemistry (3 ECTS)
- Composite materials for biomedical applications (6 ECTS)
Content
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)
Aims
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.
Pre-requiste
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)
- Calculus (properties of functions; limits and continuity; differentiation and integration)
Teaching Staff
Michele Piana, Dipartimento di Matematica, Università di Genova
Hours
24 (lectures)
Grading System
20% homework
80% oral presentation
mid-term exam: no
final-exam: yes