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Instructor:
Yejin Choi
(yejin at cs dot washington dot edu) Office hours: Wed 4:30pm  5:30pm at CSE 578 (and by appointment) 
TA:
Luheng He
(luheng at cs dot washington dot edu) Office hours: Tue 5pm  5:45pm at CSE 218 TA: Maarten Sap (msap at cs dot washington dot edu) Office hours: Thu 2pm  2:45pm at CSE 218 
Week  Dates  Topics & Lecture Slides  Notes (Required)  Textbook & Recommended Reading 

1  Mar 28, 30, Apr 1 
I. Introduction [Slides]
II. Words: Language Models (LMs) [Slides] 
LM  J&M 4.14; M&S 6 
2  Apr 4, 6, 8 
II. Words: Language Models (LMs), Smoothing [Slides]
III. Sequences: Hidden Markov Models (HMMs) [Slides] 
HMM  J&M 4.57; M&S 6 
3  Apr 11, 13, 15 
III. Sequences: Hidden Markov Models (HMMs) [Slides]
III. Sequences: PartOfSpeech Tagging (skipped) [Slides] 
Forwardbackward  J&M 5.15.3; 6.16.5; M&S 9, 10.110.3 
4  Apr 18, 20, 22  IV. Trees: Probabilistic Context Free Grammars (PCFG) [Slides]  PCFG  J&M 1314; M&S 1112 
5  Apr 25, 27, 29  IV. Trees: PCFG Grammar Refinement [Slides]  Lexicalized PCFG, Insideoutside  J&M 1314; M&S 1112 
6  May 2, 4, 6  IV. Trees: Dependency Grammars and Mildly ContextSensitive Grammars [Slides]  EdmondChuLiu;  
7  May 9, 11, 13 
V. Semantics: Frame Semantics [Slides];
V. Semantics: Distributed Semantics, Embeddings [Slides] 
J&Mv3 Vector Semantics,
Dense Vectors,
Frame Semantics 
J&M 19.4; J&M 20.7 
8  May 16, 18, 20  VI. Learning: LogLinear Models, Conditional Random Fields (CRFs) [Slides]  LogLinear, MEMMs, CRFs  J&M 6.6  6.8 
9  May 23, 25, 27  VI. Learning: Deep Learning[Slides]  Russell & Norvig Ch 18.7 ANNs; Bishop Ch 5 ANNs;  
10  (May 30), Jun 1, Jun 3  VII. Translation: Alignment Models & Phrasebased MT [Slides]  IBM Models 1 and 2, Phrase MT, EM  J&M 25; M&S 13 


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