报告摘要：Over the recent years, we have been witnessing truly remarkable progress in Machine Learning (ML) with highly visible accomplishments encountered, in particular, in natural language processing and computer vision impacting numerous areas of human endeavours. Driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, control, health care imaging, decision-making in critical areas, among others. Data are central and of paramount relevance to the design methodology and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that require urgent attention especially with the growing importance of requirements of interpretability, transparency, credibility, stability, and explainability. As a new direction, data-knowledge ML concerns a prudent and orchestrated involvement of data and domain knowledge used holistically to realize learning mechanisms and support the formation of the models. The objective of this talk is to identify the challenges and develop a unique and comprehensive setting of data-knowledge environment in the realization of the development of ML models. We review some existing directions including concepts arising under the name of physics informed ML. Key ways of elicitation and accommodation of domain knowledge are investigated. An impact on the structuralization of the ML architectures and the ensuing implications on the interpretability, explainability and credibility as well as semantic stability are studied. We investigate the representative topologies of ML models identifying data and knowledge functional modules and interactions among them. The detailed considerations on the facet of explainability including new ideas of semantic stability are covered. We also elaborate on the central role of information granularity in this area.
讲座人介绍：Witold Pedrycz received is currently a Professor and a Canada Research Chair of computational intelligence with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.He has authored or coauthored numerous papers in this area. He has also authored 15 research monographs covering various aspects of computational intelligence, data mining, and software engineering. His main research interests include computational intelligence, fuzzy modeling and granular computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledgebased neural networks, relational computing, and software engineering. Dr. Pedrycz was the recipient of a prestigious Norbert Wiener Award from the IEEE Systems, Man, and Cybernetics Society in 2007, the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. He is an Editor-in-Chief forInformation Sciences,WIREs Data Mining and Knowledge Discovery, andInternational Journal of Granular Computing. He serves on the Advisory Board ofIEEE Transactions on Fuzzy Systems. He is a member of a number of editorial boards of other international journals. He was elected a Foreign Member of the Polish Academy of Sciences in 2009. In 2012, he became a Fellow of the Royal Society of Canada. He has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing.