This workshop will address algorithms, systems, and real-world problem domains related to large-scale machine learning (“Big Learning”). With active research spanning machine learning, databases, parallel and distributed systems, parallel architectures, programming languages and abstractions, and even the sciences, Big Learning has attracted intense interest. This workshop will bring together experts across these diverse communities to discuss recent progress, share tools and software, identify pressing new challenges, and to exchange new ideas. Topics of interest include (but are not limited to):

- Big Data: Methods for managing large, unstructured, and/or streaming data; cleaning, visualization, interactive platforms for data understanding and interpretation; sketching and summarization techniques; sources of large datasets.

- Models & Algorithms: Machine learning algorithms for parallel, distributed, GPGPUs, or other novel architectures; theoretical analysis; distributed online algorithms; implementation and experimental evaluation; methods for distributed fault tolerance.

- Applications of Big Learning: Practical application studies and challenges of real-world system building; insights on end-users, common data characteristics (stream or batch); trade-offs between labeling strategies (e.g., curated or crowd-sourced).

- Tools, Software & Systems: Languages and libraries for large-scale parallel or distributed learning which leverage cloud computing, scalable storage (e.g. RDBMs, NoSQL, graph databases), and/or specialized hardware.