Announcements:
Solutions for Midterm 2 are now available.
An old midterm 2 is available here.
Please ignore the questions on probability and utility; we'll cover
these topics later in the semester. A version with answers can be
found here.
Extra FOL practice problems can be found
here.
Solutions to midterm 1 are here.
A sample midterm can be found
here . (note: ignore question 6 - we haven't covered adversarial
search yet.)
Solutions for this midterm can be found here.
A list of potential midterm topics can be found 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 24 | Lecture 1: Introduction. What is AI? | R & N: 1.1 pp 1-4, 26.1-2: pp 947-958 |
Full size Printable |
| August 29 | Lecture 2: Intro to Python | Dive Into Python Chapters 2,3,6. |
Full size Printable |
| August 31 | Lecture 3: Agents and Environments | R & N: Chapter 2: pp 32-58 |
Full size Printable |
| September 5 | Lecture 4: More Python | Dive Into Python Chapters 4,5,8. |
Full size Printable |
| September 7 | Lecture 5: Search I: BFS, DFS, IDS | R & N: 3.1-3.5 pp 59-83 |
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| September 12 | Lecture 6: Search II: Greedy Search, A* | R & N: 4.1-4.2 pp 94-109 |
Full size Printable |
| September 14 | Lecture 7: Constraint Satisfaction, Branch and Bound | R & N: 5.1-5.2 pp 137-150 |
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| September 19 | Lecture 8: Local Search and Genetic Algorithms | R & N: 4.3 pp 110-119 |
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| September 21 | Lecture 9: More GAs, midterm review | TBD | |
| September 26 | Midterm 1 | n/a | |
| September 28 | Lecture 10: Introduction to Knowledge Representation | R & N: 7.1-7.5: pp 194-220 |
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| October 3 | Lecture 11: First-order Logic | R & N: 8.1-8.3: pp 240-258 |
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| October 5 | Lecture 12: Inference and Rule-based systems | R & N: 9.1-9.6: pp 272-300 |
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| October 10 | Lecture 13: More inference, Ontologies | R & N: 10.1, 10.2, 10.5, 10.6. pp 320-328, 344-354. |
Full size Printable |
| October 12 | Lecture 14: More ontologies, using Protege. |
Ontology Development 101 The Pizza Ontology Tutorial |
Full size Printable |
| October 17 | Lecture 15: Planning | R & N: 11.1-11.3: pp 375-395 |
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| October 19 | Lecture 16: Decision Trees | R & N 18.1-18.3. pp 649-664. |
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| October 24 | Midterm review | n/a | |
| October 26 | Midterm 2 | ||
| October 31 | Lecture 17: Introduction to Probability | R & N 13.1-13.6: pp 462-481 |
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| November 2 | Lecture 18: Bayesian Networks | R & N 14.1, 14.2: pp 492-499. |
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| November 7 | Lecture 19: Bayesian Learning | R & N 20.1: pp 712-716 |
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| November 9 | Lecture 20: Introduction to Machine Learning | R & N: 20.1, 21.1: 712-716, 763-765 |
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| November 14 | Lecture 21: Clustering Algorithms | Mitchell, Chapter 8 (provided in class) |
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| November 16 | Lecture 22: Utility and Value of Information |
R & N 16.1-16.3, 16.6: pp 584-591,600-603 |
Full size Printable |
| November 21 | Lecture 23: Markov Decision Processes | R & N: 17.1-17.3: pp 613-625 |
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| November 23 | Thanksgiving. No class | n/a | |
| November 28 | Lecture 24: Neural Networks | R & N 20.5: 736-748 |
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| November 30 | Lecture 25: Neural Networks | R & N 20.5: 736-748 |
Full size Printable |
| December 5 | Lecture 26: Review and Summary | n/a |
Sample code from the intro to python lecture
Protege Resources
GA/GP readings
Python links
AIMA links