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

Important dates

Date Deadline
18-Jan Start signing up for class presentations
25-Jan Project team information due
6-Feb Review 1 due
15-Feb Project proposals due
10-Mar Review 2 due
3-Apr Project intermediate status report due
10-Apr Review 3 due
24-Apr Project final report due