题 目：Fundamentals of Machine Learning with Granular Computing: A Primer

主 讲 人：Witold Pedrycz

时 间：2023年10月20月（周五）上午9：00-10：30

地 点：腾讯会议号:940-793-266,   密码:106106

现场直播：广东省知识产权大数据重点实验室工业中心106

主讲人简介：

        Witold Pedrycz (IEEE Life Fellow) is Professor 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. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, 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, Granular Computing, and Machine Learning, among others.

        Professor Pedrycz serves as 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).  

内容提要：

       This lecture covers selected fundamentals of Machine Learning and Granular Computing to establish a conceptual and algorithmic setting for new synergistic developments at the junction of these two highly visible technologies of design and analysis of intelligent systems.

       Main architectures, learning models, and learning paradigms encountered in Machine Learning are presented in a concise and focused manner. The concepts of information granules and Granular Computing are presented by focusing on the origin and motivating factors behind the emergence of information granules. It is demonstrated how various ways of conceptualization of information granules in terms of fuzzy sets, sets, rough sets, among others lead to the realization of efficient and enhanced synergistic solutions to the classification and prediction problems encountered in Machine Learning. The design aspects of information granules are discussed.

计算机科学学院、广东省知识产权大数据重点实验室

2023年10月17日