What is a machine learning algorithm? Why is machine learning playing a primary role in physics? Which problems can be optimized using it? What is the most suitable algorithm to solve my physics problem?
These are some of the questions that this course aims to answer, providing students with the state-of-the-art knowledge regarding the usage and understanding of artificial intelligence algorithms applied to physics. The course also focuses on developing a critical comprehension of results, exploring the development of future algorithms, and the most promising technologies.
Teaching aims to develop skills in understanding and implementing machine learning algorithms applied to physics problems. Major neural algorithms will be described and discussed with various practical examples of how they are used to solve physics problems.
The course aims to provide the conceptual, theoretical, and methodological tools for a clear understanding of machine learning algorithms used in physics. To achieve this goal, the necessary techniques to comprehend the most modern neural networks and their applications to physics problems will be described and put into practice.
By the end of the course, students will be able to:
The course is self-contained. General knowledge of particle physics is useful but not necessary. Similarly, knowledge of the Python programming language is helpful (knowledge acquired during the undergraduate studies) but not essential.
The course has a strong practical component. The theory of machine learning algorithms will be discussed in lectures, and then they will be implemented in dedicated practical sessions. This approach allows students to gain a solid understanding of the concepts and then apply them directly through hands-on implementation, ensuring a comprehensive learning experience.
The course aims to:
The course encompasses these topics to provide students with a comprehensive understanding of machine learning algorithms in the context of physics applications.
Ricevimento: Student reception at DIFI (office S825) or through TEAMS, to be arranged by email (francescoarmando.dibello@unige.it)
FRANCESCO ARMANDO DI BELLO (President)
RICCARDO TORRE
ANDREA COCCARO (President Substitute)
Please refer to the calendar at the following link: https://corsi.unige.it/corsi/9012/studenti-orario
The exam consists of a written part where students will be asked to solve an exercise and an oral part to assess their understanding of the course material. The written section will test the practical application of concepts, while the oral part will evaluate their theoretical knowledge.
The committee will evaluate both the written work and the oral discussion. Both components, the written elaboration and the oral presentation, will be taken into consideration during the assessment process.
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.
