CS 311 - Fall 2009

Artificial Intelligence

Announcements

Final project list

Homework

  1. Homework 1, due Wednesday 9/16. Solutions.
  2. Homework 2, due Wednesday 9/23. Solutions.
  3. Homework 3, due Wednesday 9/30. Solutions.
  4. Homework 4, due Wednesday 10/7. Solutions.
  5. Homework 5, due Wednesday 10/21. Solutions.
  6. Homework 6, due Wednesday 10/28. Solutions.
  7. Homework 7, due Wednesday 11/4. Solutions.
  8. Homework 8, due Wednesday 11/11. Solutions.
  9. Final Project, preliminary results / progress report due Monday 11/23.

Lectures and Readings

  1. 9/7   - Course info, what is Artificial Intelligence?   (slides, A 1)
  2. 9/9   - Intro to agents and Lisp   (slides, A 2.1-2, 2.4; L 1-2)
  3. 9/11  - Environments, more Lisp   (slides, A 2.3-5; L 1-3)
  4. 9/14  - More Lisp   (Lisp log, L 1-6)
  5. 9/16  - More Lisp, iteration, format; introduction to search   (Lisp log, L 1-6; A 3.1-3)
  6. 9/18  - Uninformed search strategies   (slides, A 3.4-5)
  7. 9/21  - Informed search strategies I   (slides, A 4.1-2)
  8. 9/23  - Informed search strategies II   (slides,A 4.3-5; Applets by Tim Bahls: search and tree)
  9. 9/25  - A* search and Lisp   (hw 3)
  10. 9/28  - Game playing   (slides, A 6)
  11. 9/30  - State of the art in game playing (lecture by Tim Huang)   (slides)
  12. 10/2  - Constraint satisfaction problems   (slides, A 5)
  13. 10/5  - Logical reasoning   (slides, A 7.1-3)
  14. 10/7  - Propositional logic   (A 7.4-6)
  15. 10/9  - No class / Midterm 1
  16. 10/14 - Logical inference   (slides, A 7.4-5)
  17. 10/16 - First-order logic   (A 8.1-2)
  18. 10/19 - Inference in FoL, unification (A 9.1-2)
  19. 10/21 - Forward and backward chaining, resolution, conversion to CNF   (A 9.3-5)
  20. 10/23 - Planning, STRIPS   (slides, A 11.1-3)
  21. 10/26 - Probability theory   (A 13)
  22. 10/28 - Joint distributions, probabilistic networks   (slides, A 13.4-6, 14.1-3)
  23. 10/30 - Exact belief net inference   (A 14.2-4)
  24. 11/2  - Stochastic inference   (A 14.5)
  25. 11/4  - Decision-making, utility theory   (slides, A 16)
  26. 11/6  - Learning from observations, decision trees   (example, A 18)
  27. 11/9  - Learning decision trees, information content, performance assessment   (A 18)
  28. 11/11 - Neural nets   (slides, A 20.5)
  29. 11/13 - Perceptrons, neural net learning   (slides, A 20.5)
  30. 11/16 - Reinforcement learning, neural net and learning applications   (slides, A 21)
  31. 11/18 - Communication, speech recognition   (slides, A 22, 15.6)
  32. 11/20 - Computer vision, robotics, CSpace demo   (slides, A 24, 25)
  33. 11/23 - Philosophical foundations of AI   (slides, A 26, movie)
  34. 11/30 - Final project presentations 1
  35. 12/2  - Final project presentations 2
  36. 12/4  - Future of AI, course summary   (slides, A 27)