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Course Details
Context
Academic Year 2023/2024
Name Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology
Programme Year 1

Course Details
ID Code 061640
Course Title ONLINE LEARNING AND MONITORING
Course Type MONO-DISCIPLINARY COURSE
Credits (CFU / ECTS) 5.0
Course Description The course aims at providing students with a solid understanding of: - Incremental learning algorithms, which can be seen as the natural extension of classical ML techniques (supervised and unsupervised) to the settings where samples are provided over time and/or the data distribution is changing over time; - Change detection methods, which can be used to steer model adaptation; - Online learning algorithms, which have been developed in the reinforcement learning literature, and are specifically crafted to deal with sequential decision problems; - Domain adaptation algorithms, which can be used to adapt a pre-trained model to operate in different settings; Anomaly Detection methods. At first, we provide an overall introduction and a unifying formulation of the addressed learning problems and an overview of the involved research challenges (approximately 2 hours). Then, we illustrate different kinds of models and landmark algorithms for incremental learning in evolving environments, together with change-detection tests, which are meant to detect distribution changes and can be used to steer model adaptation (approximately 6 hours). We cover sequential decision-making problems in the framework of online learning, considering both the bandit and the expert settings (approximately 6 hours). After that, we present techniques to detect anomalous samples in the incoming data (outliers), as well as domain adaptation solutions (approximately 5 hours). Finally, we cover some landmark applications of the above-described techniques, which combine online/incremental learning algorithms with those techniques explicitly modeling the change in the environment (approximately 6 hours). In particular, an online advertising selection method, a bank fraud detection framework, and automatic ECG monitoring.
Scientific-Disciplinary Sector (SSD)
SSD Code SSD Description CFU
ING-INF/05 INFORMATION PROCESSING SYSTEMS 5.0

Details
Alphabetical group Name Teaching Assignment Details
From (included) To (excluded)
A ZZZZ Trovo' Francesco, Boracchi Giacomo
manifestidott v. 1.10.0 / 1.10.0
Area Servizi ICT
09/02/2025