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200926 COLOQUIO IA sin logos .jpg
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The Oxford Dictionary defines Artificial Intelligence (AI) as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AI has aroused widespread interest in the public and private sectors, due to the various technological developments and impacts it has had and continues to have on society. In addition, it is booming from the scientific point of view because of all the disciplines, areas, and techniques that are applied.



The National Colloquium in Artificial Intelligence seeks to bring together specialists in the areas that make up AI to give lectures on topics of their specialty. The guests are contacted by the organizing committee. The talks are scheduled for 50 minutes and are mainly addressed to researchers and graduate students.


This Colloquium is broadcast once a month, usually on Wednesdays at 12:00 pm on the YouTube links of the organizing institutions.

NEXT session

JUNE 30th, 2021


Watch live here


Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.


Dr. Albert Bifet
Professor at 
University of Waikato an IP Paris

Is Professor at University of Waikato and IP ParisPreviously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams published at MIT Press. He is one of the leaders of MOA, scikit-multiflow and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of KDD BigMine (2019-2012), and ACM SAC Data Streams Track (2021-2012).



Félix Castro


Wednesday June 30th - 12:00 hours (Central Time)

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