1. Lectures
  2. Important Dates


The slides and resources for each lecture will be updated after the class. The schedule below is tentative and both the order and the content will almost definitely change as the semester goes along.

Date Lecture
19-Jan Introduction
21-Jan Review: Supervised Learning
26-Jan Multiclass Classification: Local models
28-Jan Multiclass Classification: Global models
2-Feb First Look at Structures
4-Feb Sequence Models: HMM
9-Feb Sequence Models: Local models
11-Feb Sequence Models: CRFs
16-Feb Sequence Models: Structured Perceptron
18-Feb General Formulations: Graphical Models
23-Feb General Formulations: Markov Logic Networks
25-Feb General Formulations: Constrained Conditioned Models
2-Mar Buffer lecture
4-Mar Project planning
9-Mar Project planning
11-Mar Training strategies: Structural SVM
16-Mar Training strategies: Structural SVM, SGD
18-Mar Inference: Graph algorithms
23-Mar Inference: ILP Inference
25-Mar Inference: Approximate Inference
30-Mar Inference: Learning to Search
1-Apr Deep learning: Neural network review
6-Apr Deep learning: Recurrent Networks, LSTM
13-Apr Deep learning and structures
15-Apr Learning latent variables
20-Apr Learning with constraints
22-Apr Buffer lecture
27-Apr Practical concerns
  Looking back
5-May Project presentations during final exam (8:00 AM - 10:00 AM)

Important dates

Date Deadline
4-Feb Project team information due
18-Feb Review 1 due
25-Feb Project proposals due
16-Mar Assignment due
30-Mar Project intermediate status report due
1-Apr Review 2 due
15-Apr Review 3 due
5-May Project presentations in class
5-May Project final report due