In this era of Big Data, because of the advance of data storage and Internet technology, data become more massive, noisier and more complex. For instance, Internet itself is a very rich and huge database. The Internet data may be associated with certain structure (e.g., social networks data), may be only weakly labeled (e.g., the video and images crawled with search engine), and may be very large scale. Big Data has become ubiquitous in modern society. It poses many challenges to state-of-the-art data acquisition, computation and analysis methods. The aim of this workshop is to document recent process of Big Data technologies (e.g. Big Data Infrastructure, Distributed optimization, Stochastic optimization, MapReduce and Cloud Computing, etc.) in different real-world applications, to understand how computational bottlenecks trade-off with statistical efficiency for Big Data analysis tools, and also to stimulate discussion about potential challenges that may open new directions of learning on Big Data. We appreciate not only the manuscripts that dedicate to handle learning on Big Data, but also those which aim to discuss the approaches and/or theories for handling the new Big Data issues when exploiting massive data of different formats or structures.