The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning. This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning. Authors are invited to submit full papers with unpublished, original work of not more than 8 pages. All papers should be formatted using the ACM style (see http://www.acm.org/sigs/publications/proceedings-templates). All accepted papers (subject to post-review revisions) will be published in the ACM digital and IEEE Xplore libraries by ACM SIGHPC. For more information, visit http://ornlcda.github.io/MLHPC2016/cfp.html