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 may change as the semester goes along.

Date Lecture
8-Jan Introduction
10-Jan Review: Supervised Learning
15-Jan MLK Day. No class.
17-Jan Multiclass Classification: Local models
22-Jan Multiclass Classification: Global models
24-Jan First Look at Structures
29-Jan Sequence Models: HMM
31-Jan Sequence Models: Local models
5-Feb Sequence Models: CRFs
7-Feb Sequence Models: Structured Perceptron
  General Formulations: Graphical Models
12-Feb General Formulations: Graphical Models (continued)
14-Feb General Formulations: Markov Logic Networks
19-Feb Presidents’ Day. No class.
21-Feb General Formulations: Markov Logic Networks
26-Feb General Formulations: Constrained Conditioned Models
28-Feb Training strategies: Structural SVM
5-Mar Training strategies: Structural SVM (continued)
7-Mar Training strategies: SGD
12-Mar Inference: Graph Algorithms
14-Mar Inference: ILP
19-Mar Spring Break. No class
21-Mar Spring Break. No class
26-Mar Inference: ILP (continued)
28-Mar Inference: Approximate Inference
2-Apr Inference: Learning to Search
4-Apr Inference: Learning to Search (continued)
9-Apr Deep learning: Neural network review
11-Apr Deep learning: Recurrent Networks, LSTM
16-Apr Learning with constraints
18-Apr Learning latent variables
23-Apr Practical concerns
  Looking back
26-Apr Project presentations during final exam (10:30 AM - 12:30 PM)

Important dates

Date Deadline
17-Jan Start signing up for class presentations
24-Jan Project team information due
5-Feb Review 1 due
14-Feb Project proposals due
9-Mar Review 2 due
2-Apr Project intermediate status report due
9-Apr Review 3 due
23-Apr Project final report due