Tentative schedule of lectures:

• Lecture 1 (Thu 9/10): Introduction
• Lecture 2 (Tue 9/15): Language models
• Lecture 3 (Thu 9/17): Neural networks, applications to language models
• Lecture 4 (Tue 9/22): Topic models, EM
• Lecture 5 (Thu 9/24) Supervised and unsupervised POS tagging
• Lecture 6 (Tue 9/29): Recursive neural networks, application to tagging, sequences
• Lecture 7 (Thur 10/1): Parsing, generative, PCFGs
• Lecture 8 (Thu 10/6): Dependency parsing
• Lecture 9 (Tue 10/8): Higher order parsing, approximate learning
• Lecture 10 (Thu 10/15): Recursive neural networks for parsing
• Lecture 11 (Tue 10/20): Semantics: formulations, problems, grounding
• Lecture 12 (Thu 10/22): Syntactic and semantic embeddings
• MIDTERM EXAM (Tue 10/27): in class
• Lecture 13 (Thu 10/29): Discourse, segmentation
• Lecture 14 (Tue 11/3): Information extraction
• Lecture 15 (Thu 11/5): Machine translation: traditional approaches
• Lecture 16 (Tue 11/10): Machine translation: neural networks
• Project prepation meetings (Thu 11/12)
• Project prepation meetings (Tue 11/17)
• Project prepation meetings (Thu 11/19)
• Project prepation meetings (Tue 11/24)
• Lecture 17 (Tue 12/1): Guest lecture
• Lecture 18 (Thu 12/3): Guest lecture
• Poster presentation (Tue 12/8)
• Poster presentation (The 12/10)