| W | Date | Topic | Slides | Assign | Collect | |
| 1 | T, 1/16 |
Brief course overview Brief introduction to AI |
ch01-4pp.ps
ch01.ps ch01.pdf ch01.ppt |
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| R, 1/18 |
Ch. 11: Machine Learning: Connectionist
Biological neurons Artificial neurons (perceptrons) A learning example |
ch11a-4pp.ps
ch11a.ps ch11a.pdf ch11a.ppt |
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| 2 | T, 1/23 |
Ch. 11: Machine Learning: Connectionist
Perceptron learning algorithm In class exercise on perceptron learning Introduction to neural networks |
handout.ps
handout.pdf |
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| R, 1/25 |
Ch. 11: Machine Learning: Connectionist
Backpropagation algorithm for neural networks Deriving the learning formulas Backpropagation learning example Summary and comments |
errcorr.ps
errcorr.pdf errcorr-4pp.ps |
hw1: neural networks
assigned hw1: details |
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| 3 | T, 1/30 | |||||
| R, 2/1 | Ch. 2: Predicate Calculus |
ch02-4pp.ps
ch02.ps ch02.pdf ch02.ppt |
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| 4 | T, 2/6 | |||||
| R, 2/8 | No class (Winter Carnival) | |||||
| 5 | T, 2/13 | |||||
| R, 2/15 |
hw2: logic
(postscript file) assigned |
|||||
| 6 | T, 2/20 |
(MTU Job Fair is on this day.)
Ch. 13: Automated Reasoning |
ch13-4pp.ps
ch13.ps ch13.pdf ch13.ppt |
hw1: neural networks
collected hw1: details |
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| R, 2/22 | Ch. 3: Structures and Strategies for State Space Search |
ch03-4pp.ps
ch03.ps ch03.pdf ch03.ppt Keweenaw map local copy small map (.jpg) small map (.ps) small map (.pdf) |
hw2: logic
(postscript file) collected |
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| 7 | T, 2/27 | |||||
| R, 3/1 | Exam 1 | |||||
| 8 | T, 3/6 |
Ch. 3: Structures and Strategies for State Space Search
Formulating search problems |
handout.ps
handout.pdf |
hw3: uninformed search
assigned |
||
| R, 3/8 | ||||||
| T, 3/13 | No class (Spring Break) | |||||
| R, 3/15 | No class (Spring Break) | |||||
| 9 | T, 3/20 | |||||
| R, 3/22 | Ch. 4: Heuristic search |
ch04-4pp.ps
ch04.ps ch04.pdf ch04.ppt |
hw4: search
(postscript file) assigned |
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| 10 | T, 3/27 |
hw4: search
(postscript file) collected |
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| R, 3/29 |
Ch. 8: Strong Method Problem Solving
Part b: Planning |
ch8b-4pp.ps
ch8b.ps ch8b.pdf ch8b.ppt |
hw3: uninformed search
collected |
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| 11 | T, 4/3 |
hw5: planning
assigned |
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| R, 4/5 | Exam 2 | |||||
| 12 | T, 4/10 |
Ch. 10: Machine Learning: Symbol Based
Part a: Version Spaces |
ch10a-4pp.ps
ch10a.ps ch10a.pdf ch10a.ppt |
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| R, 4/12 |
Ch. 10: Machine Learning: Symbol Based
Part b: Learning Decision Trees |
ch10b-4pp.ps
ch10b.ps ch10b.pdf ch10b.ppt |
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| 13 | T, 4/17 |
Ch. 10: Machine Learning: Symbol Based
Part c: Unsupervised Learning (Clustering) |
ch10c-4pp.ps
ch10c.ps ch10c.pdf ch10c.ppt |
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| R, 4/19 |
Ch. 10: Machine Learning: Symbol Based
Part c: Unsupervised Learning (Clustering-continued) |
ch10c2-4pp.ps
ch10c2.ps ch10c2.pdf ch10c2.ppt |
hw6: machine learning, uncertainty
assigned |
hw5: planning
collected |
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| 14 | T, 4/24 | Ch. 9: Reasoning in Uncertain Situations |
ch09-4pp.ps
ch09.ps ch09.pdf ch09.ppt |
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| R, 4/26 | Ch. 5, Sec. 9.3: Stochastic Methods |
ch05-09-4pp.ps
ch05-09.ps ch05-09.pdf ch05-09.ppt |
hw6: machine learning, uncertainty
collected |
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| F | T, 5/1 | Final Exam time: 3:00 pm - 5:00 pm place: Rekhi 214 (same as the classroom) |
Good luck! |
grades:
( grade list in xhtml ) ( grade list in pdf ) |
