Difference between revisions of "Syllabus for Structured Prediction 10-710 in Fall 2011"
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* Tues 11/22. [[Class Meeting for 10-710 11-22-2011|Regularizers derived from unannotated data]]. (William). | * Tues 11/22. [[Class Meeting for 10-710 11-22-2011|Regularizers derived from unannotated data]]. (William). | ||
* Thus 11/24 -- ''Thanksgiving, no class'' | * Thus 11/24 -- ''Thanksgiving, no class'' | ||
| − | * Tues 11/29. [[Class Meeting for 10-710 11-29-2011|Classification on graphs]] ( | + | * Tues 11/29. [[Class Meeting for 10-710 11-29-2011|Classification on graphs]] (guest lecture by Frank Lin, Ni Lao) |
* 11/30. '''Deadline for November round of writeups.''' | * 11/30. '''Deadline for November round of writeups.''' | ||
== December == | == December == | ||
| − | * Thus 12/1. Bayesian approaches to structured models (Noah). | + | * Thus 12/1. [[Class Meeting for 10-710 12-01-2011|Bayesian approaches to structured models]]. (Noah). |
* Tues 12/6. Project presentations 1 | * Tues 12/6. Project presentations 1 | ||
* Thus 12/8. Project presentations 2. | * Thus 12/8. Project presentations 2. | ||
* Mon 12/12. '''Final projects due.''' | * Mon 12/12. '''Final projects due.''' | ||
Latest revision as of 15:09, 1 December 2011
This is the syllabus for Structured Prediction 10-710 in Fall 2011. The slides available may be out of date (from the last version of the class) but will be updated after each lecture.
Contents
August/September
- Tues 8/30. Introduction/Overview (Noah)
- Thus 9/1. HMMs as structured-output prediction models. (William)
- Tues 9/6. HMMs to MEMMs. (William)
- Thus 9/8. CRFs. (William).
- Mon 9/12. Add a short project proposal to the wiki (even one sentence might be enough).
- Tues 9/13. Pseudo-likelihood inference. (William).
- Thus 9/15. Understanding "conditional" models. (Noah).
- Mon 9/19. Join or recruit a 2-3 person project team and add that information to your project proposal.
- Tues 9/20. Perceptrons and margin-based learning. (William).
- Thus 9/22. Ranking perceptrons and margin-based learning. (William).
- Mon 9/26. Identify the baseline method(s) and dataset you will use in your project.
- Tues 9/27. Stacking, Searn, and related "meta-learning" techniques. (William).
- Thus 9/29. Generalizing SVM to structures. (Noah; note: this is Rosh Hashanah)
- Fri 9/30. First set of four wiki assignments due. Brendan has posted some detailed guidelines. Some more information on this was given in beginning of the lecture on Sept 1.
October
- Tues 10/4. Making structured predictions with dynamic programming (Noah).
- Thus 10/6. Inference with ILP and other methods (Noah).
- Thus 10/6. Complete your project proposal by explaining what your "big idea" will be.
- Tues 10/11. Natural language parsing with CFGs (Noah).
- Thus 10/13. Dependency parsing. (Noah).
- Tues 10/18. EM and Learning edit distances 1. (William).
- Thus 10/20. EM and Learning edit distances 2. (William).
- Tues 10/25. Mid-term project reports - short presentations of task, dataset, and baseline.
- Thus 10/27. Weighted FSTs. (Noah).
November
- Tues 11/1. Unsupervised grammar induction. (Noah).
- Wed 11/2. Deadline for October round of writeups.
- Thus 11/3. Alignment and translation (guest lecture by Kevin Gimpel).
- Tues 11/8. Kernels for structured inputs. (William).
- Thus 11/10. Kernels for structured outputs. (Noah).
- Tues 11/15. Discriminative latent-variable models. (Noah).
- Thus 11/17. Information extraction (guest lecture by Brendan O'Connor).
- Tues 11/22. Regularizers derived from unannotated data. (William).
- Thus 11/24 -- Thanksgiving, no class
- Tues 11/29. Classification on graphs (guest lecture by Frank Lin, Ni Lao)
- 11/30. Deadline for November round of writeups.
December
- Thus 12/1. Bayesian approaches to structured models. (Noah).
- Tues 12/6. Project presentations 1
- Thus 12/8. Project presentations 2.
- Mon 12/12. Final projects due.
