| W | Date | Topic | Slides | Assign | Collect |
| 1 | M, 1/09 | Brief course overview Brief introduction to AI Ch. 2: Predicate Calculus |
ch01-4pp.ps
ch01.ps ch01.pdf ch01.ppt |
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| W, 1/11 | Ch. 2: Predicate Calculus |
ch02-4pp.ps
ch02.ps ch02.pdf ch02.ppt |
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| F, 1/13 | hw1: logic | ||||
| 2 | M, 1/16 | no class (Martin Luther King, Jr. Day) | |||
| W, 1/19 | |||||
| F, 1/20 | Ch. 13: Automated Reasoning |
ch13-4pp.ps
ch13.ps ch13.pdf ch13.ppt |
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| 3 | M, 1/23 | hw2: resolution | hw 1 due | ||
| W, 1/25 | Ch. 3: Structures and Strategies for State Space Search |
ch03-4pp.ps
ch03.ps ch03.pdf ch03.ppt |
hw3: search | ||
| F, 1/27 | Ch. 3: Structures and Strategies for State Space Search
The farmer, wolf, goat, and cabbage problem |
The search tree:
fwgc.pdf fwgc.ps fwgc.fig |
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| 4 | M, 1/30 | hw 2 due | |||
| W, 2/01 | |||||
| F, 2/03 | |||||
| 5 | M, 2/06 | Ch. 4: Heuristic search |
ch04-4pp.ps
ch04.ps ch04.pdf ch04.ppt | ||
| W, 2/08 | hw4: α-β pruning | hw 3 due | |||
| F, 2/10 | no class (Winter Carnival Recess) | ||||
| 6 | M, 2/13 | ||||
| W, 2/15 | Ch. 6: Building Control Algorithms for State Space Search |
ch06-4pp.ps
ch06.ps ch06.pdf ch06.ppt |
hw4 due | ||
| F, 2/17 | Midterm 1 | Good luck! | |||
| 7 | M, 2/20 | Ch. 7: Knowledge Representation |
ch7-4pp.ps
ch7.ps ch7.pdf ch7.ppt |
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| W, 2/22 | Ch. 8: Strong Method Problem Solving
Part a: Expert Systems |
ch8a-4pp.ps
ch8a.ps ch8a.pdf ch8a.ppt |
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| F, 2/24 | |||||
| 8 | M, 2/27 | Return exam 1, discuss the solutions ( grades ) | ch8a class notes | ||
| W, 3/01 | Ch. 8: Strong Method Problem Solving
Part b: Planning |
ch8b-4pp.ps
ch8b.ps ch8b.pdf ch8b.ppt |
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| F, 3/03 | |||||
| B | 3/04-11 | Spring Break | |||
| 9 | M, 3/13 | hw5: planning | |||
| W, 3/15 | |||||
| F, 3/17 | hw6: knowledge repr. machine learning |
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| 10 | M, 3/20 | Ch. 10: Machine Learning: Symbol Based
Part a: Version Spaces |
ch10a-4pp.ps
ch10a.ps ch10a.pdf ch10a.ppt |
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| W, 3/22 | Ch. 10: Machine Learning: Symbol Based
Part b: Learning Decision Trees |
ch10b-4pp.ps
ch10b.ps ch10b.pdf ch10b.ppt |
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| F, 3/24 | hw6 (Questions 1 and 2) due |
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| 11 | M, 3/27 | Ch. 10: Machine Learning: Symbol Based
Part c: Unsupervised Learning (Clustering) |
ch10c-4pp.ps
ch10c.ps ch10c.pdf ch10c.ppt |
hw6 (Question 3) due |
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| W, 3/29 | hw5 due | ||||
| F, 3/31 | Midterm 2 | Good luck! | |||
| 12 | M, 4/03 | Ch. 10: Machine Learning: Symbol Based
Part c: Unsupervised Learning (Clustering-continued) |
ch10c2-4pp.ps
ch10c2.ps ch10c2.pdf ch10c2.ppt |
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| W, 4/05 | Ch. 11: Machine Learning: Connectionist
Part 1 |
ch11a-4pp.ps
ch11a.ps ch11a.pdf ch11a.ppt sample Lisp perceptron sample output 1 sample output 2 |
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| F, 4/07 | Ch. 11: Machine Learning: Connectionist
Multilayered neural networks |
handout.pdf
handout.ps handout.tex |
hw7: neural networks | ||
| 13 | M, 4/10 | Ch. 9: Reasoning in Uncertain Situations |
ch09-4pp.ps
ch09.ps ch09.pdf ch09.ppt | ||
| W, 4/12 | hw8: uncertainty | ||||
| F, 4/15 |
example.pdf
example.ps example.tex |
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| 14 | M, 4/17 | Ch. 5, Sec. 9.3: Stochastic Methods |
ch05-09-4pp.ps
ch05-09.ps ch05-09.pdf ch05-09.ppt | ||
| W, 4/19 | hw 8 due | ||||
| F, 4/21 | Wrap up, AI research |
lastclass-4pp.ps
lastclass.ps lastclass.pdf lastclass.ppt | hw 7 due | ||
| F | W, 4/26 | Final Exam time: 12:45 pm - 2:45 pm place: Rekhi 214 (same as the classroom) topics: list grades: ( grade list ) |
Good luck! |