DaSSWeb, Data Science and Statistics Webinar
'DATA SCIENCES FOR THE XXI CENTURY'
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Nowadays, there are applications in which the data are modelled best not as persistent tables, but rather as transient data streams. In this talk, we discuss the limitations of current machine learning and data mining algorithms. We discuss the fundamental issues in learning in dynamic environments like learning decision models that evolve over time, learning and forgetting, concept drift, and change detection. Data streams are characterized by huge amounts of data that introduce new constraints in the design of learning algorithms: limited computational resources in terms of memory, processing time, and CPU power. In this talk, we present some illustrative algorithms designed to take these constraints into account. We identify the main issues and current challenges that emerge in learning from data streams and present open research lines for further developments
João Gama is a Full Professor at FEP, University of Porto, Portugal. He is also a senior researcher and member of the board of directors of the Laboratory of Artificial Intelligence and Decision Support (LIAAD), a group belonging to INESC Porto.
João Gama serves is serving as a member of the Editorial Board of Machine Learning Journal, Data Mining and Knowledge Discovery, Intelligent Data Analysis, KAIS, TKDE, and New Generation Computing. He served as Cochair of ECML 2005, ECMLPKDD 2015, DS09, ADMA09, IDA2011, EPIA 2017, DSAA 2017 and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. His main research interest is in knowledge discovery from data streams, networked, and evolving data. He has an extensive list of publications in the area of data stream learning.