Course objectives
This course aims at elucidating the deep links between mathematical optimisation and machine learning. The students will be introduced to the following themes and abilities: understand the properties of different kinds of non-linear continuous optimisation algorithms to tune the (hyper-)parameters of machine learning models and how to apply them; understand how to model some advanced machine learning models as bi-level optimisation models; identify situations where machine learning and process mining can be used as a sub-module for combinatorial optimisation problems.
Course delivery
The lectures consist of 24 hours divided between the three themes previously described (respectively 10h/10h/4h) and will take place on Wednesdays afternoons between 1pm and 5pm on the following dates: 2/4/2025, 9/4/2025, 16/4/2025, 30/4/2025, 7/5/2025 and 14/5/2025 at the Computer Science Department of the University of Turin. For students who cannot attend physically, the lectures will be available in streaming
Further details: https://dott-informatica.campusnet.unito.it/do/corsi.pl/Show?_id=z84t
- Teacher: Roberto Aringhieri
- Teacher: Alessandro Druetto
- Teacher: Pierre Hosteins