Schedule

UNIVERSITY BREAKS AND IMPORTANT DATES

Please visit the MIT Registrar Calendar for the latest dates:

  • Sept 4 (Wed): Semester begins
  • Sept 13 (Fri): Last day for seniors and grad students to change H1 subjects to/from P/D/F
  • Sept 20 (Fri): Student holiday (no classes)
  • Oct 14 (Mon): Indigenous People’s Day (no classes)
  • Nov 11 (Mon): Veteran’s Day (no classes)
  • Nov 20 (Wed): Drop date. Last day to cancel full-term subjects.
  • Nov 28 (Thurs) - Nov 29 (Fri): Thanksgiving Break
  • Dec 11 (Wed): Last day of classes
  • Dec 16 (Mon) - Dec 20 (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 5 (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 3 (Tues)
    • We will not have class on Oct 15 (Tues) and Nov 28 (Thurs), due to university breaks
    • Final Presentations will be on Dec 5 (Thurs) and Dec 10 (Tues)
  • Homeworks are due Thursdays 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
    • Project proposals are due on Oct 15 (Tues)
    • Project progress reports are due on Nov 12 (Tues)
    • All posters must be uploaded on Dec 5 (Thurs)
    • Poster sessions will take place on Dec 5 (Thurs) and Dec 10 (Tues)
    • Final projects are due on Dec 10 (Tues)

LECTURES

  • Lecture 1: Introduction + ML Basics
  • Lecture 2: Classification (linear models, neural nets)
  • Lecture 3: Sequence models 1 (ngrams, log-linear LMs, word2vec)
  • Lecture 4: Sequence models 2 (RNNs)
  • Lecture 5: Sequence models 3 (seq2seq + attention)
  • Lecture 6: Transformers
  • Lecture 7: Pretraining 1 (BERT and GPT)
  • Lecture 8: Pretraining 2 (SFT and RLHF)
  • Lecture 9: Efficient training (MoE, quantization, LoRA)
  • Lecture 10: Doing research
  • Lecture 11: Decoding 1 (prompting, CoT, and agents)
  • Lecture 12: Decoding 2 (search and sampling)
  • Lecture 13: Multimodality
  • Lecture 14: Midterm review
  • Lecture 15: Midterm
  • Lecture 16: Beyond transformers (SSMs, Mamba, …)
  • Lecture 17: NLP Engineering
  • Lecture 18: Interpretability
  • Lecture 19: Guest Lecture: Speech
  • Lecture 20: Struct pred
  • Lecture 21: Guest Lecture: Intellectual property (SERC)
  • Lecture 22: Bias & fairness
  • Lecture 23: Human language processing
  • Lecture 24: Conclusion

Quick Due Dates

  • 9/19 HW1
  • 10/3 HW2
  • 10/15 Project Proposal
  • 10/17 HW3
  • 10/29 Midterm
  • 11/12 Project Progress Report
  • 12/5 Poster upload
  • 12/5 & 12/10 Poster sessions
  • 12/10 Final project