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TAs and Office Hours:
Danning Zheng: Monday 4pm-5.30pm, computer science TA room
Mukund Yelahanka Raghuprasad: Wednesday 11am-12.30pm, computer science TA room
Vu Anh Phung: Thursday 4pm-5.30pm, computer science TA room
Zhuoran Liu: Friday 10am-11.30am, computer science TA room
Announcements:
Past midterms for the class are here: fall 2011, fall 2012, fall 2013, fall 2014, fall 2017, spring 2018.
| Date | Topics | Video Lectures | References | Flipped Classroom Materials | |
| Week 1 (September 3rd-7th) | Introduction to NLP, Language Modeling (Slides: we will cover slides 1-50 inclusive) |
Video lectures in Courseworks: All of Module 1-2; All of Module 3; Sections 4.1, 4.2 in Module 4. | Sections 1.1-1.4.1 inclusive of
Notes on language modeling (required reading). |
Questions (part 1), Solutions (part 1) Questions (part 2), Solutions (part 2) | Week 2 (Sept 10th-14th) | Tagging, and Hidden Markov Models (Slides) | All videos in Module 6 in courseworks: The tagging problem (10:01) to Summary (1:50) inclusive. | Notes on tagging problems, and hidden Markov models (required reading) |
Questions, Solutions |
| Week 3 (Sept 17th-21st) | Log-Linear Models (Slides) | All videos in Module 15 on Courseworks. | Notes on Log-Linear Models (required reading) |
Questions, Solutions, Past midterm question | |
| Week 4 (Sept 24th-28th) | Parsing, and Context-free Grammars (Slides) | Courseworks videos: [1] All of Module 7; [2] Module 8, sections 8-1 to 8-3 inclusive | Questions, Solutions | ||
| Week 5 (Oct 1st-5th) | Probabilistic Context-free Grammars (continued), and lexicalized context-free grammars (Slides part 1) (Slides part 2), (Slides part 3) | Courseworks videos: [1] Module 8, sections 8-4 to 8-6; [2] All of Module 9; [3] All of Module 10 | Notes on Probabilistic Context-Free Grammars (required reading) |
Questions, Solutions | |
| Week 6 (Oct 8th-12th) | Log-Linear Models for Tagging, and for history-based parsing (Slides part 1), (Slides part 2). | Modules 16 and 17 in courseworks. | Notes on MEMMs (Log-Linear Tagging Models) (required reading) |
Questions on CRFs, solutions are in section 4 of this note. Additional questions, Solutions | |
| Week 7 (Oct 15th-19th) | Feedforward Neural Networks (Slides) | Module 22 videos in Courseworks.
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Notes on Feedforward Neural Networks (required reading) |
Questions, Solutions | |
| Week 8 (Oct 22nd-26th) | Mid-term week | Note: no flipped classrooms this week. | |||
| Week 9 (Oct 29th-Nov 2nd) | Computational Graphs, and Backpropagation (Slides) | Module 23 videos in Courseworks.
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Notes on Computational Graphs, and Backpropagation (required reading) |
Questions, Solutions | |
| Week 10 (Nov 5th-9th) | Word Embeddings in Feedforward Networks; Tagging and Dependency Parsing using Feedforward Networks (Slides) | Module 24 videos in Courseworks.
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| Week 11 (Nov 12th-16th) | Recurrent Networks, and LSTMs, for NLP (Slides) | Module 25 in Courseworks. | Questions, Solutions | ||
| Week 12 (Nov 26th-30th) | Recurrent Networks, and attention, for statistical machine translation (Slides) | Module 26 in Courseworks. | Questions, Solutions | ||
| Week 13 (Dec 3rd-7th) | Brown Clustering, and Word2Vec (Brown Clustering Slides), (Word2Vec Slides). | Module 18 in Courseworks. | Word2Vec paper (we will cover in the flipped classroom section) | Questions, Solutions |