Corsi Anno Accademico 2024/2025
With the ongoing explosion of data production and availability of devices with computational capabilities, traditional centralized data-processing techniques start to suffer from the increasing costs of data transfer, infrastructure, security and privacy needs. The edge/fog computing paradigm has emerged to meet these challenges, particularly for Internet of Things and open Cyber-Physical Systems. It processes data where it is generated in order to address the shortcomings of centralized techniques. However, decentralized architectures have their own challenges: handling device unreliability, data volatility, and coping with conficting goals. Aggregate Programming is a recent abstraction allowing one to program large-scale networks in a simple way, while providing strong guarantees on the resilience of the resulting behaviour under changes and unreliability. This abstraction shifts the local viewpoint of single device behaviour, to the global viewpoint of overall system behaviour; leaving the (automated) global-to-local translation to language implementation. This short course presents the Aggregate Programming abstraction, together with its commonly used distributed algorithms that have strong resilience guarantees, and its toolkit (programming language and simulator).
- Teacher: Giorgio Audrito
- Teacher: Ferruccio Damiani
- Teacher: Gianluca Torta
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
- Introduction to the course: overview on emotion oriented systems
- Emotions: concepts, definitions and models: from psychological theories to
computational models
- Linguistic and semantic resources: datasets, sentiment lexicons (multilingualism),
ontologies; collecting and labelling methodologies; available benchmarks and
standards.
- Application I: Emotions in interaction: emotions in virtual agents; practical
architectures for emotion generation and expression
- Application II: Emotion detection and sentiment analysis in social media:
state-of-the-art methodologies, application domains
- Teacher: Cristina Bosco
- Teacher: Rossana Damiano
- Teacher: Viviana Patti
See https://dott-informatica.campusnet.unito.it/do/corsi.pl/Show?_id=gu2e
- Teacher: Marco Guazzone
- Teacher: Valerio Basile
- Teacher: Roberto Esposito
- Teacher: Attilio Fiandrotti
- Teacher: Mirko Polato