Data with temporal (or sequential) structure arise in several applications, such as speaker diarization, human action segmentation, network intrusion detection, DNA copy number analysis, and neuron activity modelling, to name a few. A particularly recurrent temporal structure in real applications is the so-called change-point model, where the data may be temporally partitioned into a sequence of segments delimited by change-points, such that a single model holds within each segment whereas different models hold accross segments. Change-point problems may be tackled from two points of view, corresponding to the practical problem at hand: retrospective (or "a posteriori"), aka multiple change-point estimation, where the whole signal is taken at once and the goal is to estimate the change-point locations, and online (or sequential), aka quickest detection, where data are observed sequentially and the goal is to quickly detect change-points. The purpose of this workshop is to bring together experts from the statistics, machine learning, signal processing communities, to address a broad range of applications from robotics to neuroscience, to discuss and cross-fertilize ideas, and to define the current challenges in temporal segmentation.