Schedule

UNIVERSITY BREAKS AND IMPORTANT DATES

Please visit the Harvard 10-year Calendar for the latest dates:

  • Sept 1 (Wed): Semester begins
  • Sept 6 (Mon): Labor Day (no classes)
  • Oct 11 (Mon): Indigenous People’s Day (no classes)
  • Oct 20 (Wed): Kamala Harris’ birthday (classes are held)
  • Nov 11 (Thurs): Veteran’s Day (classes are held)
  • Nov 24 (Wed) - Nov 28 (Sun): Thanksgiving Break
  • Dec 2 (Thurs): Last day of classes/lectures
  • Dec 3 (Wed) - Dec 8 (Wed): Reading Period (no classes)
  • Dec 9 (Thurs) - Dec 18 (Sat): Exam Period

OUR CLASS

  • Lectures are on Tuesdays and Thursdays at 9:45am-11:00am in SEC LL2.221
    • first lecture is on Sept 2 (Thurs). Mandatory attendance for anyone wanting to enroll.
    • earlier lectures will mostly concern Deep Learning models
    • later lectures will mostly concern NLP Tasks
    • the last content-based lecture will be Nov 11 (Thurs)
    • the Exam will be given during the following lecture time, Nov 16 (Tues)
    • four lectures after this are designated for Open Project Discussions, whereby we will check-in with Research Project groups and offer assistance and feedback
    • we will not have class on Nov 25 (Thurs) and Dec 7 (Tues), due to university breaks
    • Final Presentations (pre-recorded videos) will be on Dec 9 (Thurs)
  • ~8 unannounced pop quizzes will be issued, each of which in-class at the beginning of a lecture.
  • Homeworks will be released on Tuesdays and will be due two weeks later (Mondays at 11:59pm EST).
  • Research Projects will span 12 weeks of the semester, with several deliverables due throughout:
    • Phase 1 is due Sept 30 (Thurs) @ 11:59pm EST
    • Phase 2 is due Oct 14 (Thurs) @ 11:59pm EST
    • Phase 3 is due Oct 28 (Thurs) @ 11:59pm EST
    • Phase 4 is due Nov 11 (Thurs) @ 11:59pm EST
    • Phase 5 is due Nov 23 (Tues) @ 11:59pm EST
    • Final Presentations (pre-recorded videos) will be on Dec 9 (Thurs)

LECTURES

SPREADSHEET FORMAT

  • The HW # column denotes which lectures are eligible for comprising the content for each homework (i.e., each homework encompasses four lectures)
  • Lecture topics with a white background represent foundational content and Deep Learning models
  • Lecture topics with a gray background represent NLP tasks
  • Lecture topics with an orange background represent guest lectures