As data generated from sciences, business, governments, etc. are reaching petabyte or even exabyte at an alarming rate, theories, models, and applications in online learning is becoming important in machine learning to process a large amount of streaming data effectively and efficiently. Recently, a number of online learning algorithms have been proposed to tackle the issues of ultra-dimension and high imbalance among the data. In this talk, we focus on new developments of online learning technologies in both theory and applications. Important topics including online boosting, online learning for sparse learning models, and distributed online algorithms, etc. will be discussed. Moreover, some of our recent works such as online learning for multi-task feature selection, imbalanced data, online dictionary learning, etc., will also be presented to demonstrate how online learning approaches can effectively handle streaming big data.

Irwin King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing for Big Data. In these research areas, he has over 225 technical publications in journals and conferences. In addition, he has contributed over 30 book chapters and edited volumes. Prof. King is Associate Dean (Education), Faculty of Engineering and Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He is also Director of the Shenzhen Key Laboratory of Rich Media and Big Data. Recently, Prof. King has been an evangelist in the use of education technologies in eLearning for the betterment of teaching and learning.

• Presentation is available here.