This document summarizes structured prediction and structured large margin estimation approaches. It discusses how structured prediction can model complex, correlated outputs like sequences, trees, and matchings. It presents a min-max formulation that casts structured prediction as a linear program for inference, allowing joint training with large margin methods. This provides tractable learning for problems like conditional random fields, context-free grammars, and associative Markov networks.