![]() 8/27 intro, hmm review, (readings: ch 1) 8/29 writing, hmm review 2 Unit 1: INFERENCE 9/03 decoding 1, (readings: ch 2.1, 2.2) 9/05 decoding 2, (readings: ch 2.3) 9/10 constituent parsing 9/12 dependency parsing 9/17 probability distributions 9/19 soft inference (readings: ch 5.1, 5.2) 9/24 soft inference (cont'd) 9/26 minimum Bayes risk decoding 10/1 approximate inference: local search 10/3 approximate inference: Markov Chain Monte Carlo 10/8 Lagrangian relaxation 10/10 interlude: experimentation (readings: appendix B) 10/15 supervised learning basics (readings: ch 3.3) 10/17 no class Unit 2: SUPERVISED LEARNING 10/22 supervised learning basics (cont'd) (readings: ch 3.3) 10/24 generative models 10/29 conditional models (readings: ch 3.4, 3.5) 10/31 large margin training Unit 3: UNSUPERVISED LEARNING 11/5 learning from incomplete data 11/7 [peer paper reading workshop] 11/12 [guest lecture: planning] 11/14 expectation-maximization (readings: ch 4.1) code! 11/19 expectation-maximization (cont'd) 11/21 guest lecture: gene finding 11/26 unsupervised learning with features (readings: ch 4.2) 11/28 no class 12/3 Bayesian inference 12/5 Bayesian models: examples The wiki used last time this course was offered can be found here. How to use LaTeX on google sites. Book: Linguistic Structured Prediction (LSP) |