Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and the construction of algorithms which learn from data to make decisions for future data. Such algorithms build a model from example inputs to make data-driven predictions or decisions, rather than following strictly static program instructions.

“Big data” refers to the datasets being so large and complex, that becomes difficult to process using conventional database management and data analysis tools. Big data introduces great challenges to data collection and storage technologies. These challenges include capturing, storing, searching, sharing, transferring, analyzing, and visualizing big data. Learning the underlying knowledge from big data is one of the most important issues of big data researches. Big data brings an unprecedented challenge to traditional theories and algorithms of machine learning. The proper representation and effective handling of the underlying knowledge in big data yield a significant influence on the storage and the usage of big data.

In order to share the latest progresses, current challenges, and potential applications of machine learning for big data, we are very pleased to propose this special session in IEEE SMC-2016.