Invited Talk at the 2nd HKUST-USC Joint Workshop on Big Data Applications, Hong Kong

Online Learning and Online Learning

Irwin King

Department of Computer Science and Engineering
The Chinese University of Hong Kong


This talk will be in two parts. The first part will be related to online learning as in machine learning and the second part will be dealing with topics in education analytics for online learning in Massive Open Online Course (MOOC), University Open Online Course (UOOC), Small Personal Online Course (SPOC), flipped classroom, etc. Online learning is a promising technique for big data analytics, especially for learning from streaming data. One important property of online learning is that it can adaptively update the parameters of learning models when a new sample appears. This can avoid retraining from scratch. In the first part of the talk, I will give two novel online learning models on 1) how to adaptively update the weights of the models while selecting features among multiple tasks and 2) how to adaptively seek nonlinear classifiers when two classes of data are imbalanced. Our proposed online learning for multi-task feature selection and kernelized online imbalanced learning are two tools to solve these two issues, respectively. Formulation, algorithms, theory, and experimental results are presented accordingly. Big Education is the convergence of Big Data in education as these are two hot topics of intense research and discussion in recent years. In the second part of the talk, I will introduce a new project that is being funded by the Hong Kong SAR Government named, Knowledge and Education Exchange Platform (KEEP). The KEEP portal is a knowledge aggregator and technology integrator that provides access to online educational resources for producing positive teaching and learning experiences to the educators and students.

Research Interests

Prof. King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing. In these research areas, he has over 200 technical publications in journals and conferences. He is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD) and Journal of Neural Networks. He is a both member of the Board of Governors and Vice-President for INNS and APNNA. Moreover, he is the General Chair of WSDM2011, General Co-Chair of RecSys2013, ACML2015, and in various capacities in a number of top conferences such as WWW, NIPS, ICML, IJCAI, AAAI, etc. 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. Recently, he was on leave with AT&T Labs Research, San Francisco and was also teaching Social Computing and Data Mining as a Visiting Professor at UC Berkeley. He received! his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.

Presentation Materials

confs/bigdataapplications2014.txt · Last modified: 2014/12/18 08:35 by admin     Back to top