PhD Course: Online Learning and Monitoring (2022)

Descrizione

This course provides an overview of Machine Learning (ML) methods that are meant for streaming data and that force the learner to operate in an online or incremental manner. These settings are often encountered in real-world applications, e.g., to select sponsored links for Internet advertising, or to detect frauds in credit card transaction. The online setting poses relevant challenges to classical data-driven solutions since i) the model has to integrate new pieces of information as soon as they become available, ii) the learning algorithm has to adapt to the current operating conditions, iii) the learning algorithms have to be computationally efficient, to be executed in real-time.

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