Schedule

UNIVERSITY BREAKS AND IMPORTANT DATES

Please visit the MIT Registrar Calendar for the latest dates:

  • Sept 6 (Wed): Semester begins
  • Sept 15 (Fri): Last day for seniors and grad students to change H1 subjects to/from P/D/F
  • Sept 22 (Fri): Student holiday (no classes)
  • Oct 9 (Mon): Indigenous People’s Day (no classes)
  • Nov 10 (Fri): Veteran’s Day (no classes)
  • Nov 22 (Wed): Drop date. Last day to cancel full-term subjects.
  • Nov 23 (Thurs) - Nov 24 (Fri): Thanksgiving Break
  • Dec 13 (Wed): Last day of classes
  • Dec 18 (Mon) - Dec 22 (Fri): Exam Period (no classes)

OUR CLASS

  • Lectures are on Tuesdays and Thursdays at 11am-12:30am in 32.123
    • first lecture is on Sept 7 (Thurs).
    • earlier lectures will mostly concern representation and Deep Learning models
    • later lectures will include guest lectures about particular NLP Problems/Tasks
    • the last content-based lecture will be Dec 5 (Tues)
    • we will not have class on Oct 10 (Tues) and Nov 23 (Thurs), due to university breaks
    • Final Presentations will be on Dec 7 (Thurs) and Dec 12 (Tues)
  • Homeworks are due (Mondays at 11:59pm EST) and you will have roughly 2 weeks to complete them.
  • Research Projects will span 12 weeks of the semester, with several deliverables due throughout

LECTURES

  • Lecture 1: Introduction + ML Basics (Logistics Regression; SGD)
  • Lecture 2: Text Classification (linear classifier; BoW; TFIDF)
  • Lecture 3: Word Representations (matrix factorization; word2vec)
  • Lecture 4: Language Modelling (MLP; RNN)
  • Lecture 5: Attention
  • Lecture 6: Transformers Part 1
  • Lecture 7: Transformers Part 2
  • Lecture 8: Large Language Models (LLMs) Part 1
  • Lecture 9: Large Language Models (LLMs) Part 2
  • Lecture 10: Structured Models: Hidden Markov Models (HMMs)
  • Lecture 11: Structured Models: Trees
  • Lecture 12: Structured Models: Conditional Random Fields (CRFs)
  • Lecture 13: Structured Models: Latent Variable Models
  • Lecture 14: Mid-term Review
  • Lecture 15: Doing Research
  • Lecture 16: NLP Engineering
  • Lecture 17: Guest lecture: Ethics and NLP
  • Lecture 18: Interpretability
  • Lecture 19: Guest Lecture: Speech
  • Lecture 20: Guest Lecture: Human Language Processing
  • Lecture 21: Guest Lecture: SERC Ethics
  • Lecture 22: Guest lecture: TBD
  • Lecture 23: Conclusion