Title: Adaptive Machine Learning for Data Streams

Invited Speaker: Prof. Albert Bifet

Abstract:

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.

Short Bio:

Albert Bifet is Professor at University of Waikato, and Institut
Polytechnique de Paris. Previously 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
SAMOA software environments for implementing algorithms and running
experiments for online learning from evolving data streams. 

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Title: Interactive Machine Learning

Invited Speaker: Prof. Andrea Passerini

Abstract:

With artificial intelligence and machine learning becoming
increasingly more pervasive in our societies and everyday lives, there
is a growing need for interactive approaches bringing the human in the
loop and allowing systems to adapt to the needs and specificities of
each user. In this talk I will present some promising frameworks for
interactive machine learning, highlighting their pros and cons, and
discussing open challenges towards truly human-centric and personalized
learning systems.

Short bio:

Andrea Passerini is Associate Professor at the Department of
Information Engineering and Computer Science (DISI) of the University
of Trento and Adjunct Professor at Aalborg University. He is director
of the Structured Machine Learning Group and coordinator of the
Research Program on Deep and Structured Machine Learning, both at
DISI. His research interests include structured machine learning,
neuro-symbolic integration, explainable and interactive machine
learning, preference elicitation and learning with constraints. He
co-authored over 130 refereed papers, including 49 journal articles,
and he regularly publishes at top AI conferences and journals like
IJCAI, AAAI, AIJ, MLJ and DAMI. He is President of the Steering
Committee of ECMLPKDD, the main European conference on machine
learning and data mining. He was invited speaker at various
conferences and workshops (NeSy16, MLDM18, DA2PL18, KSEM19, M-PREF20,
M-PREF22) for his research on constructive preference elicitation,
interactive machine learning and the combination of learning and
reasoning. He is Principal Investigator for the University of Trento
in the H2020 ICT-48-2020 project TAILOR: Foundations of Trustworthy AI
– Integrating Reasoning, Learning and Optimization.