CS 311 - Fall 2003

Artificial Intelligence


Announcements

Final projects

Final exam

Solutions to Midterm 1 and Midterm 2.

Homework

  1. Homework 1, due Wednesday 9/17 in class. Solutions.
  2. Homework 2, due Monday 9/29 in class. Solutions.
  3. Homework 3, due Wednesday 10/8 in class. Solutions.
  4. Homework 4, due Wednesday 10/22 in class. Solutions.
  5. Homework 5, due Friday 10/31 at 4pm. Solutions.
  6. Homework 6, due Monday 11/10 at 4pm. Solutions.
  7. Homework 7, due Monday 11/17 at 4pm. Solutions.
  8. Final Project, 8-minute demos in class Friday 12/5; final reports due Saturday 12/6 at midnight.

Lectures and Readings

  1. 9/8   - What is Artificial Intelligence?   (Ch 1)
  2. 9/10  - Intro to agents and Lisp   (Ch 2.1-3; Lisp text Ch 1-3)
  3. 9/12  - More agents and Lisp   (Ch 2.3-5; Lisp text Ch 1-3)
  4. 9/15  - Symbolic programming   (Lisp text Ch 1-6)
  5. 9/17  - More Lisp; introduction to search   (Lisp text Ch 1-6; Ch 3.1-3)
  6. 9/19  - Uninformed search strategies   (Ch 3.4-5)
  7. 9/22  - Informed search strategies I   (Ch 4.1-2)
  8. 9/24  - Informed search strategies II   (Ch 4.3-5)
  9. 9/26  - Game playing   (Ch 6)
  10. 9/29  - Constraint satisfaction problems   (Ch 5)
  11. 10/1  - Logical reasoning   (Ch 7.1-6)
  12. 10/3  - Man and Machine - Redrawing the Boundary (video)
  13. 10/6  - [Announcing new instructor]
  14. 10/8  - First-order logic (Ch 8.1-2)
  15. 10/10 - Using first-order logic, situation calculus (Ch 8.3-5, 9.1, 10.3)
  16. 10/13 - Logical inference, unification, chaining (Ch 9.1-4)
  17. 10/15 - Resolution, conversion to CNF (Ch 9.5)
  18. 10/20 - Resolution example, planning, STRIPS (Ch 9.5, 11)
  19. 10/22 - Probability theory (Ch 13)
  20. 10/24 - Joint distributions, probabilistic networks (Ch 14.1-3)
  21. 10/27 - Inference in probabilistic networks (Ch 14.4-5,8)
  22. 10/29 - Stochastic inference (Ch 14.5)
  23. 10/31 - Decision-making, utility theory (Ch 16)
  24. 11/3  - Learning from observations, decision trees (Ch 18)
  25. 11/5  - Learning decision trees, information content, performance assessment (Ch 18)
  26. 11/7  - Neural nets (Ch 20.5)
  27. 11/10 - Perceptrons, neural net learning (Ch 20.5)
  28. 11/12 - Reinforcement learning, neural net and learning applications (Ch 21)
  29. 11/14 - Communication, syntactic and semantic analysis (Ch 22)
  30. 11/17 - Speech recognition (Ch 15.6)
  31. 11/19 - Probabilistic language models, information retrieval (Ch 23)
  32. 11/21 - Machine translation, computer vision (Ch 23.4, 24)
  33. 11/24 - Robotics, CSpace demo (Ch 25)
  34. 12/1  - Philosophical Foundations of AI (Ch 26)
  35. 12/3  - Future of AI, course summary (Ch 27)
  36. 12/5  - Final project presentations