复杂系统先进控制与智能自动化秋季国际研讨会系列报告之（二十四 ~ 二十九）
报告形式：教学综合楼B0506，ZOOM
报 告 人：Witold Pedrycz，加拿大阿尔伯塔大学教授
（University of Alberta，Canada）
报告主题：Intelligent systems and Computing
（一）
报告时间：2023年11月13日（星期一）10:05 – 11:40
报告地点：教学综合楼B0506
主要内容：Ⅰ. Machine Learning, Information Granules, and Granular Computing
Introductory notes, motivation
Fundamental performance measures of constructs of Machine Learning
Key challenges of Machine Learning
Information granules – formal representation environments
Interval calculus
Fuzzy sets
Operations on information granules
（二）
报告时间：2023年11月15日（星期三）10:05 – 11:40
报告地点：教学综合楼B0506
主要内容：Ⅱ. Granular Computing: knowledge acquisition problems
Method of pairwise comparison
Clustering methods: taxonomy, example algorithms, interpretation of results,
Comparative analyses
Principle of justifiable granularity and classification architectures
（三）
报告时间：2023年11月30日（星期四）10:05 – 11:40
报告地点：教学综合楼B0506，ZOOM（ID：980 7954 9546，Passcode: 422471）
主要内容：Ⅲ. Interpretable and Explainable Machine Learning
Basic notions
Local interpretable models -agnostic exploration (LIME)
Counterfactual explanation
Brittleness of Machine Learning models
Feature importance- assessment methods
（四）
报告时间：2023年12月18日（星期一）10:05 – 11:40
报告地点：教学综合楼B0506，ZOOM（ID：992 2067 8023，Passcode: 791111）
主要内容：Ⅳ. Rule-based computing
Functional rules and mixture of experts: analysis and basic design
Quality of rules for prediction and classification
Rule-based classifiers
Fuzzy neurocomputing
（五）
报告时间：2024年1月4日（星期四）10:05 – 11:40
报告地点：教学综合楼B0506，ZOOM（ID：982 2025 1478，Passcode: 266427）
主要内容：Ⅴ. Credibility of Machine Learning models
Motivating arguments
Confidence and prediction information granules and main relationships
Gaussian process model
Granular embedding
（六）
报告时间：2024年1月8日（星期一）10:05 – 11:40
报告地点：教学综合楼B0506，ZOOM（ID：947 7441 8569，Passcode: 803376）
主要内容：Ⅵ. Federated Learning, Transfer Learning, and knowledge distillation
Algorithms of federated learning- main ideas and design schemes (averaged and gradient based federated learning)
Transfer learning – towards sustainable learning
Knowledge distillation- learning schemes for classification and regression
报告人简介: Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.
His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He has published numerous papers in these areas; the current h-index is 114 (Google Scholar) and 87 on the list top-h scientists for computer science and electronics http://www.guide2research.com/scientists/. He is also an author of 21 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering.
Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer). He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.
