The following is a tentative list (a superset, actually) of the topics to be covered in the class.

*Note*: This list will change as the semester progresses. For a listing of
lectures, visit the schedule 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 inter-related 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
- Semi-supervised learning, indirect supervision and response-driven learning

- Dealing with computational complexity of inference
- Approximate inference
- Lagrangian relaxation and dual decomposition

- Structured Prediction and Deep Neural Networks
- Structure, features and representations