Computational, Simulation and Machine Learning Methods in High Energy Physics and Beyond: Machine Learning
A.Y. 2022/2023
Course offered to students on the PhD programme in
Visit the PhD website for the course schedule and other information
Lead instructor: Stefano Carrazza
An introduction to machine learning techniques including model representation, parameter learning, non-linear models, hyperparameter tune, and an overview of modern deep learning strategies. The seminars will cover the theoretical and mathematical aspects of machine learning followed by practical examples of code implementation using public frameworks.
Statistics, basics of programmin languages.
Mandatory for students of the Phd programme in " Physics, Astrophysics and Applied Physics" at first year
Mandatory for students of the Phd programme in " Physics, Astrophysics and Applied Physics" at first year
Assessment methods
Giudizio di approvazione
Assessment result
superato/non superato
How to enrol
Deadlines
The course enrolment deadline is usually the 27th day of the month prior to the start date.
How to enrol
- Access enrolment on PhD courses online service using your University login details
- Select the desired programme and click on Registration (Iscrizione) and then on Register (Iscriviti)
Ignore the option "Exam session date” that appears during the enrolment procedure.
Contacts
For help please contact [email protected]
Professor(s)