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Lecture schedule

  • Wednesday, Jan 21: Introduction
  • Monday, Jan 26: Neural networks / Deep learning (part 1a)
  • Wednesday, Jan 28: Neural networks / Deep learning (part 1b)
  • Monday, Feb 2: Neural networks / Deep learning (part 2a)
  • Wednesday, Feb 4: Neural networks / Deep learning (part 2b)
  • Monday, Feb 9: Jupyter/Python
  • Wednesday, Feb 11: Exam 1 / Reinforcement learning (part 1a)
  • Monday, Feb 16: No Class, Presidents' Day
  • Tuesday, Feb 17: Reinforcement learning (part 1b)
    • ⚠️ Legislative Monday (Tuesday follows Monday schedule)
    • 📥 Homework 1 due
  • Wednesday, Feb 18: Reinforcement learning (part 2a)
  • Monday, Feb 23: Reinforcement learning (part 2b)
  • Wednesday, Feb 25: Reinforcement learning (part 3a)
  • Monday, Mar 2: Reinforcement learning (part 3b)
  • Wednesday, Mar 4: Bayesian modeling (part 1a)
  • Monday, Mar 9: Bayesian modeling (part 1b)
  • Wednesday, Mar 11: Bayesian modeling (part 2a)
    • 📥 Project proposal is due
  • Monday, Mar 16: No Class, Spring Break
  • Wednesday, Mar 18: No Class, Spring Break
  • Monday, Mar 23: Bayesian modeling (part 2b)
    • 📥 Homework 3 due
  • Wednesday, Mar 25: Categorization (part a)
  • Monday, Mar 30: Categorization (part b)
    • 📥 Homework 4 due
  • Wednesday, Apr 1: Probabilistic graphical models (part a)
  • Monday, Apr 6: Probabilistic graphical models (part b)
  • Wednesday, Apr 8: Causal interventions and active learning (part a)
  • Monday, Apr 13: Causal interventions and active learning (part b)
  • Wednesday, Apr 15: Program induction and language of thought models (part a)
  • Monday, Apr 20: Program induction and language of thought models (part b)
  • Wednesday, Apr 22: Computational theory of mind
  • Monday, Apr 27: Conclusion
  • Wednesday, Apr 29: TBD
  • Monday, May 4: TBD
    • 📥 Final project due