The following is a tentative list (a superset, actually) of the topics to be covered in the class.
Note: This list will change as we get closer to the start of the semester and also as the semester progresses. For a listing of lectures and schedules, visit the schedules page.
 Introduction
 What is a structure? Examples
 What are the different computational challenges for structured prediction?
 Modeling, training and inference
 Review of linear models for binary and multiclass
classification
 Linear models for binary classification
 Perceptron, Logistic Regression and Support Vector Machine for binary classification
 From binary classification to multiclass classification, models of multiclass classification
 Loss functions, online vs batch learning
 Binary and Multiclass classification to structures
 Simple extensions
 Inference: Predicting multiple interrelated variables
 Binary and multiclass classification as special structures
 Basic structured prediction models: Sequence labeling
 Inference with classifiers
 Hidden Markov Models
 Conditional Random Fields for sequences
 Structured Perceptron for sequences
 Inference with sequences
 General conditional models
 Connection to Markov Random Fields and Bayes Nets
 Extension of structured perceptron and conditional random fields to general conditional models
 Structured Support Vector Machines
 Tradeoffs between different learning algorithms
 Inference in general conditional models
 Combinatorial optimization
 Dynamic programming
 Framing inference as integer linear programs: Constrained conditional models
 Modeling background knowledge
 Tradeoffs between different training regimes
 Training with or without inference
 Constraint driven learning, posterior regularization

General recipe for fully supervised learning
 Learning without full supervision
 Modeling latent variables in conditional models
 Semisupervised learning, indirect supervision and responsedriven learning
 Dealing with computational complexity of inference
 Approximate inference
 Lagrangian relaxation and dual decomposition
 Structured Prediction and Deep Neural Networks