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Week 1:Class Introduction and Outline.

Week 2:HMMs for IE & Named Entity Extraction

Week 3:Maximum Entropy Classification

• Some references for Limited Memory BFGS:
  • Unconstrained Optimization: a good introduction to unconstrained optimization, includes a couple pages on BFGS: pdf
  • BFGS: detailed description of BFGS and comparison with DFP (BFGSs cousin):citeseer
  • L-BFGS: Jorge Nocedal (aka Mr. L-BFGS) and company describe L-BFGS and its implementation in great detail. pdf,ps

Week 4:Conditional Finite State Models

• Rabiner's HMM Tutorial: pdf

Other References

• A Brief Introduction to Graphical Models and Bayesian Networks
• Murphy, K., Y. Weiss, and M. Jordan. (1999). Loopy belief propogation for approximate inference: an empirical study. In Proc. of the Conf. on Uncertainty in AI. online
• Yedidia, J., W. Freeman and Y. Weiss. (2001). Generalized belief propagation. In NIPS 13, pages 689-695. MIT Press. online

From 3/7/2003:

continuation of the derivation of Gain from the della Pietra, della Pietra, Lafferty paper, section III: (solving for alpha) pdf, ps

• • Set ALLOWTOPICCHANGE = FernandoDiaz, HemaRaghavan, AronCulotta, BrentHeeringa, JenNeville, VanessaMurdock, JeremyPickens, AlvaroBolivar, DavidStracuzzi, JoshLewis, BenWellner, AliciaWolfe