Revealing and modeling the hidden state-space of dynamical systems is a fundamental problem in signal processing, control theory, and learning. Classical approaches to this problem include hidden Markov models, reinforcement learning, and various system identification algorithms. More recently, the problem has been approached by such modern machine learning techniques as kernel methods, Bayesian and Gaussian processes, latent variables, and the information bottleneck. Moreover, dynamic state-space learning is the key mechanism in the way organisms cope with complex stochastic environments, i.e., biological adaptation. One familiar example of a complex dynamic system is the authorship system in the NIPS community. Such a system can be described by both internal variables, such as links between NIPS authors, and external environment variables, such as other research communities. This complex system, which generates a vast number of papers each year, can be modeled and investigated using various parametric and non-parametric methods. In this workshop we intend to review and confront different approaches to dynamical system learning, with various applications in machine learning and neuroscience. In addition, we hope this workshop will familiarize the machine learning community with many real-world examples and applications of dynamical system learning. Such examples will also serve as the basis for the discussion of such systems in the workshop.