Announcements:
Topics for the final exam are now available.
Here are some extra FOL questions for you to practice with.
Topics for midterm 2 are now available.
Topics for midterm 1 are now available.
A Sample midterm 1 is now available. Answers are here.
Go here
to sign up for the cs662 mailing list. (you must be on this
list - please use an email address you check regularly.)
Regrading:
If you feel that your homework was not graded correctly, you
should return it to me, along with a written explanation of the
error. I will evaluate this explanation and award any necessary points.
Lectures and associated readings:
| Date | Topic | Associated Reading | Slides |
| August 23 | Lecture 1: Introduction. What is AI? | R & N: 1.1 pp 1-4, 26.1-2: pp 947-958 |
Full size Printable |
| August 28 | Lecture 2: Intro to Python | Dive Into Python Chapters 2,3,6. |
Full size Printable |
| August 30 | Lecture 3: Agents and Environments | R & N: Chapter 2: pp 32-58 |
Full size Printable |
| September 4 | Lecture 4: More Python | Dive Into Python Chapters 4,5,8. |
Full size Printable |
| September 6 | Lecture 5: Search I: BFS, DFS, IDS | R & N: 3.1-3.5 pp 59-83 |
Full size Printable |
| September 11 | Lecture 6: Search II: Greedy Search, A* | R & N: 4.1-4.2 pp 94-109 |
Full size Printable |
| September 13 | Lecture 7: Intro to Information Retrieval |
R & N: 23.2 pp 840-848 Baeza-Yates & Ribero-Neto: 2.5 (provided in class) |
Full size Printable |
| September 18 | Lecture 8: Intro to Natural Language Processing | R & N: 22.2-22.5 pp 795-818 |
Full size Printable |
| September 20 | Lecture 9: Statistical NLP |
R & N: : 23.1, 23.3 pp 834-840, 848-850 Manning and Schütze, Ch 11,12 (provided in class) |
Full size Printable |
| September 25 | Lecture 10: Introduction to Knowledge Representation | R & N: 7.1-7.5: pp 194-220 |
Full size Printable |
| September 27 | Midterm 1 | ||
| October 2 | Lecture 11: First-order Logic | R & N: 8.1-8.3: pp 240-258 |
Full size Printable |
| October 4 | Lecture 12: Inference and Rule-based systems | R & N: 9.1-9.6: pp 272-300 | |
| October 9 | Lecture 13: Ontologies | R & N: 10.1, 10.2, 10.5, 10.6. pp 320-328, 344-354. |
Full size Printable |
| October 11 | Lecture 14: More ontologies |
Ontology Development 101 The Pizza Ontology Tutorial |
|
| October 16 | Lecture 15: Planning I | R & N: 11.1-11.3: pp 375-395 |
Full size Printable |
| October 19 | Lecture 16: Planning II | R & N: 12.2-12.6: pp 422-449 |
Full size Printable |
| October 23 | Lecture 17: Decision Trees | R & N 18.1-18.3. pp 649-664. |
Full size Printable |
| October 25 | Lecture 18: Probability I | R & N 13.1-13.6: pp 462-481 |
Full size Printable |
| October 30 | Lecture 19: Probability II | R & N 14.1, 14.2: pp 492-499. |
Full size Printable |
| November 1 | Lecture 20: Bayesian Learning | R & N 20.1: pp 712-716 |
Full size Printable |
| November 6 | Midterm II | n/a | |
| November 8 | Lecture 21: Utility and Value of Information | R & N 16.1-16.3, 16.6: pp 584-591,600-603 |
Full size Printable |
| November 13 | Lecture 22: Markov Decision Processes | R & N: 17.1-17.3: pp 613-625 |
Full size Printable |
| November 15 | Lecture 22: Markov Decision Processes | R & N: 17.1-17.3: pp 613-625 |
Full size Printable |
| November 20 | Lecture 24: Clustering Algorithms | R & N: 20.1, 20.4, 21.1: 712-716, 733-736, 763-765 |
Full size Printable |
| November 22 | Thanksgiving. No class | n/a | |
| November 27 | Lecture 25: Reinforcement Learning | R & N 21.1-21.3: pp 763-777 |
Full size Printable |
| November 29 | Lecture 26: Neural Networks I | R & N 20.5: 736-748 |
Full size Printable |
| December 4 | Lecture 27: Neural Networks II | R & N 20.5: 736-748 |
Full size Printable |
Sample Python Code