Learning from Data Streams: Theory and practices in online machine learning
Hassan Nazeer Chaudhry
DEIB PhD student - Politecnico di Milano
DEIB - 2A Room (Building 20, second floor)
March 28th, 2019
2.30 pm
Contacts:
Hassan Nazeer Chaudhry
Research Line:
Computer Science and Engineering
DEIB PhD student - Politecnico di Milano
DEIB - 2A Room (Building 20, second floor)
March 28th, 2019
2.30 pm
Contacts:
Hassan Nazeer Chaudhry
Research Line:
Computer Science and Engineering
Sommario
The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modelling methods: In the data stream model, older data is no longer available to revise earlier suboptimal modelling decisions as the fresh data arrives. In my presentation, I will give an overview of machine learning algorithms and models for online learning, focusing on classification, regression, clustering, and show how they can be implemented in stream processing systems.