報告題目：Causal relationship exploration and applications
報告人：Professor Jiuyong Li
報告時間：2019年03月29日10：00
報告地點：望江校區(qū)第三教學(xué)樓503會議室
報告內(nèi)容：Various machine learning methods make use of association relationships for classification and decision making. An association shows that two variables exhibit the same (or opposite) trend but may not indicate that the two variables have an inherent relationship. In other words, an association relationship can be spurious and/or conditional. Causal relationship discovery is to find the inherent relationships where the change of one variable leads to the change of another. The identification of casual relationships is crucial for understanding data and supports evidence based decision making. Causal discovery is a central task for science, health, economy and nearly all areas of studies. In this talk, I will discuss our work in the area and applications.
報告人簡介：Dr Jiuyong Li is a Professor and an Associate Head of School at the School of Information Technology and Mathematical Sciences of University of South Australia. He leads the Data Analytics Group in the School. His main research interests are in data mining, bioinformatics, and data privacy. His research work has been supported by Australian Research Council and Cooperative Research Centre for many years. He has published more than 150 papers, mostly in leading journals and conferences in the areas. He is a member of the Australian Computer Society National Committee for Artificial Intelligence. He has been a chair (or a PC chair) of multiple Australasian Data Mining and Artificial Intelligence conferences and actively serving PC (and senior PC) member for many international conferences in data mining. He has received senior visiting fellowships from Nokia Foundation, the Australian Academy of Science, and Japan Society of Promotion of Science.
歡迎廣大師生踴躍參加！
永利yl23411官網(wǎng)
2019年3月28日
當(dāng)前位置：