CS287: Natural Language Processing

Harvard University

Course Info

Instructor
Alexander "Sasha" Rush <srush@seas.harvard.edu>
OH: Wed 1-4, MD 217
Teaching Assistants
Course Email
Lectures
Tues/Thurs 2:30-4pm Jefferson 256
Sections
Links

Announcements

  • The nn tutorial will be Friday (2/12) at 2pm, in MD 223
  • The Torch tutorial will be tomorrow (1/29) at 2pm, in Pierce 320

Schedule

Date Area TopicReadings Notes Assignment
Jan. 26 Natural Language Processing Introduction HW1 (Repo) (Kaggle)
Jan. 28 Text Classification Background and Naive Bayes
Jan. 29 Section Torch Notes Torch
Feb. 2 Multiclass Logistic Regression
Feb. 4 Hinge and Optimization YG § 6-6.1, , optim
Feb. 9 Neural Networks Tagging + Bilinear Models YG § 3, § 4.1-4.5, HW2 (Repo) (Kaggle)
Feb. 11 Neural Networks YG § 5
Feb. 12 Section Torch nn Notes Torch nn
Feb. 16 Word Embeddings (notebook) YG § 5
Feb. 18 Language Modeling Background
Feb. 23 Neural Language Models HW3 (Repo) (Kaggle)
Feb. 25 Convolutions YG § 9
Mar. 1 Midterm
Mar. 3 Recurrent Neural Networks Fundamentals YG § 10 Projects (Wiki)
Mar. 8 LSTMs and Language Modeling YG § 11
Mar. 10 NLP Applications Applications HW4 (Repo) (Kaggle)
Mar. 22 Structure and Search Background
Mar. 24 Viterbi and Structured Perc
Mar. 25 Section Torch rnn Notes Elements rnn
Mar. 29 Marginals
Mar. 31 CRF
Apr. 5 Topics Statistical Machine Translation HW5 (Repo) (Kaggle)
Apr. 7 Neural Machine Translation
Apr. 8 Section DP Notes
Apr. 12 Topic QA1 (Semantic Parsing)
Apr. 14 QA2 (Neural)
Apr. 19 Conclusion
May 3 Final Project Presentations
May 12 Projects Due

Grading

Grades are determined by four aspects of the class:

  • Five assignments (50%)
  • In-class exam (20%)
  • Final project (25%)
  • Class participation (5%)

Citations