Representation learning has developed at the crossroad of different disciplines and application domains. It has recently enjoyed enormous success in learning useful representations of data from various application areas such as vision, speech, audio, or natural language processing. It has developed as a research field by itself with several successful workshops at major machine learning conferences, sessions at the main machine learning conferences (e.g., 3 sessions on deep learning at ICML 2013 + related sessions on e.g. tensors or compressed sensing) and with the recent ICLR (International Conference on Learning Representations) whose first edition was in 2013. We take here a broad view of this field and want to attract researchers concerned with statistical learning of representations, including matrix- and tensor-based latent factor models, probabilistic latent models, metric learning, graphical models and also recent techniques such as deep learning, feature learning, compositional models, and issues concerned with non-linear structured prediction models. The focus of this workshop will be on representation learning approaches, including deep learning, feature learning, metric learning, algebraic and probabilistic latent models, dictionary learning and other compositional models, to problems in real-world data mining. Papers on new models and learning algorithms that combine aspects of the two fields of representation learning and data mining are especially welcome. This one-day workshop will include a mixture of invited talks, and contributed presentations, which will cover a broad range of subjects pertinent to the workshop theme. Besides classical paper presentations, the call also includes demonstration for applications on these topics. We believe this workshop will accelerate the process of identifying the power of representation learning operating on semantic data.