Structured prediction is the problem of making a joint set of decisions to optimize a joint loss. There are two families of algorithms for such problems: Graphical model approaches and learning to search approaches. Graphical models include Conditional Random Fields and Structured SVMs and are effective when writing down a graphical model and solving it is easy. Learning to search approaches, explicitly predict the joint set of decisions incrementally, conditioning on past and future decisions. Such models may be particularly useful when the dependencies between the predictions are complex, the loss is complex, or the construction of an explicit graphical model is impossible. We will describe both approaches, with a deeper focus on the latter learning-to-search paradigm, which has less tutorial support. This paradigm has been gaining increasing traction over the past five years, making advances in natural language processing (dependency parsing, semantic parsing), robotics (grasping and path planning), social network analysis and computer vision (object segmentation).