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Academic Year |
2023/2024 |
Name |
Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology |
Programme Year |
1 |
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)
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SSD Code
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SSD Description
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CFU
|
ING-INF/05
|
INFORMATION PROCESSING SYSTEMS
|
5.0
|
|
Alphabetical group
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Name
|
Teaching Assignment Details
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From (included)
|
To (excluded)
|
A
|
ZZZZ
|
Trovo' Francesco, Boracchi Giacomo
|
|
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