Syllabus

Topics (some changes possible):

Note: References to papers, dates, and speakers are provided in a Google Docs document

• Introduction into structured prediction: problems, settings, etc (given by Ivan)
• Hidden Markov models (Ivan)
• Structured perceptron (Ivan)
• Local models: Maximum entropy Markov models
• Conditional random fields (sequence labeling / segmentation settings)
• SVM: binary, multilable and structured settings (SVM-Struct)
• Maximum margin Markov networks (M3Ns)
• Combining learning and search: SEARN and predecessors
• Parsing: weighted context-free grammars (CFGs): generative vs discriminative training
• Parsing: transition-based vs global models (in dependency parsing context)
• Parsing: CFGs with latent annotation
• learning with latent representation of the context
• Semi-supervised methods for structured prediction