Notes

  • Course taught by Christian Tarsney (christian.tarsney@utoronto.ca)
  • TA: Micheal Lanc (micheal.lanc@mail.utoronto.ca)
  • OH: Thurs 1:15-2:15PM KW384 and by appointment
  • Tutorials are primarily for discussions
    • Attendance: 50%
    • Participation: students offer high quality contributions
  • Quizzes 15%
  • Tutorial participation 10%
  • After-class reflections 15%, you can choose to:
    • Opinion about a topic
    • Opinion about readings
    • Raise a point of confusion
  • Midterm exam 20%
  • Final exam 40%

Midterm

  • 10 multiple choice
  • 10 Fill in the blank
  • 5 Vocabulary questions (what is deep learning, what is intetionality)
  • 3 short answers
    • (Describe one reason for denying LLM might have genuine representation of things in external world)
    • Explain heddens argument that statistical fairness criteria besides CWG arent genuine requirements

Concepts

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6