| W | Date | Topic | Slides | Assign | Collect |
| 1 | M, 1/09 |
Course overview Brief introduction to AI Intelligent agents |
ch01.pdf
ch02.pdf |
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| W, 1/11 | Ch. 18, Section 7: Neural Networks |
ch18-07.pdf
nn-algorithm.pdf nn-example.pdf |
hw1: neural networks
assigned |
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| 2 | M, 1/16 | No class (Martin Luther King, Jr. Day) | |||
| W, 1/18 | Ch. 18, Sections 6 and 7: Analysis of Neural Networks |
ch18-06-07.pdf
Example on spreadsheet |
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| 3 | M, 1/23 | Ch. 3, Sections 1 -- 4: Search, Uninformed Search | ch03-a.pdf | Homework 1, interim report is due | |
| W, 1/25 | Ch. 3, Sections 1 -- 4: Search, Uninformed Search (cont'd) | ||||
| 4 | M, 1/30 | Ch. 3, Sections 5 -- 6: Informed (Heuristic) Search | ch03-b.pdf |
hw2: search
assigned |
Homework 1 is due |
| W, 2/01 | No class (NSF meeting) | ||||
| 5 | M, 2/06 | Ch. 4, Section 1: Local Search Algorithms and Optimization Problems | ch04.pdf | Homework 2 is due | |
| W, 2/08 | Hui Meen's presentation on using genetic algorithms for optimizing space trajectories | ||||
| 6 | M, 2/13 | Ch. 5: Adversarial Search |
ch05.pdf ch05-examples.pdf |
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| W, 2/15 | Ch. 07 - Logical Agents | aima-ch07.pdf | |||
| 7 | M, 2/20 | Ch. 07 - Logical Agents (cont'd) | hw3: alpha-beta pruning | ||
| W, 2/22 | Ch. 08 - First-Order Logic | aima-ch08.pdf | Homework 3 is due | ||
| R, 2/23 | Exam 1 Time: 6:00pm-7:30pm Place: Fisher 328 Topics covered (textbook and slides): Chapter 18 Learning from Examples 18.6 Regression and Classification with Linear Models 18.7 Artificial Neural Networks Chapter 3 Solving Problems by Searching 3.1 Problem-Solving Agents 3.2 Example Problems 3.3 Searching for Solutions 3.4 Uninformed Search Strategies 3.5 Informed (Heuristic) Search Strategies 3.6 Heuristic Functions Chapter 4 Beyond Classical Search 4.1 Local Search Algorithms and Optimization Problems Chapter 5 Adversarial Search 5.1 Games 5.2 Optimal Decisions in Games 5.3 Alpha-Beta Pruning 5.3 Imperfect Real-Time Decisions The sections that are part of chapters 18, 3, 4, and 5, but are not listed above will not be included in the exam. |
Good luck! | |||
| 8 | M, 2/27 | Ch. 09 - Inference in First-Order Logic | ch09.pdf | ||
| W, 2/29 | Classes after 3:00pm cancelled at MTU due to snow storm | ||||
| - | M, 3/05 | No class (Spring Break) | |||
| W, 3/07 | No class (Spring Break) | ||||
| 9 | M, 3/12 | Ch. 09 - Inference in First-Order Logic (cont'd) |
hw4: logic
assigned |
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| W, 3/14 | Ch. 18, Section 3: Learning Decision Trees | ch18-03.pdf | |||
| 10 | M, 3/19 |
Ch. 19, Section 1: Knowledge in Learning Version space learning |
ch19-01.pdf |
hw5: Decision tree learning
assigned |
Homework 4 is due |
| W, 3/21 |
Ch. 18, supplemental Clustering |
clustering.pdf | |||
| 11 | M, 3/26 | Ch. 12, Section 6: Dealing with default information | ch12-06.pdf | Homework 5, interim report due | |
| W, 3/28 |
Ch. 13: Quantifying uncertainty
Probabilistic reasoning |
ch13.pdf | |||
| R, 3/29 | Exam 2 Time: 6:00pm-7:30pm Place: Fisher 329 Topics covered (textbook and slides): Chapter 7 Logical Agents Chapter 8 First-Order Logic Chapter 9 Inference in First-Order Logic |
Good luck! | |||
| 12 | M, 4/02 |
Ch. 13: Quantifying uncertainty (cont'd)
Bayesian rule Bayesian belief networks (BBNs) |
Homework 5 is due | ||
| W, 4/04 |
Ch. 13: Quantifying uncertainty (cont'd)
Bayesian belief networks (BBNs) |
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| 13 | M, 4/09 |
Ch. 10: Classical planning
Computers versus Common Sense (by Doug Lenat for Google) |
ch10.pdf
Link to Google Video |
hw6: uncertainty
assigned |
|
| W, 4/11 | Ch. 10: Classical planning (cont'd) |
hw7: computational intelligence
assigned |
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| 14 | M, 4/16 | Homework 6 is due | |||
| W, 4/18 | Homework 7 is due | ||||
| F | W, 4/25 | Final Exam on Wednesday, 4/25 time: 3:00pm-5:00pm place: Fisher 131 (our classroom) ( OSSR's final exam schedule) Final Exam Topics covered: Chapter 18 - Learning from Examples 18.3 Learning Decision Trees Ch. 19 - Knowledge in Learning 19.1 A Logical Formulation of Learning Chapter 18 - Learning from Examples (supplemental) clustering Chapter 12 - Knowledge Representation 12.6 Reasoning with Default Information Chapter 14 - Probabilistic Reasoning 14.73 Representing vagueness: Fuzzy sets and fuzzy logic Ch. 13 - Uncertainty Ch. 14 - Probabilistic Reasoning Bayesian Belief Networks Ch. 10 - Classical Planning |
Good luck! |