| Week | Date | Topic/Read Before Class | Slides | Assignment | Collect |
| 1 | M, 01/13 | Course overview, go over the syllabus,
Brief introduction to AI Intelligent agents |
ch01.pdf
ch02.pdf |
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| W, 01/15 | Ch. 18, Sections 6,7: Neural Networks | ch18-06-07.pdf | |||
| F, 01/17 | Ch. 18, Sections 6,7: Neural Networks (cont'd) |
nn-algorithm.pdf
nn-example.pdf perceptron-example-spreadsheet.ods (Open Office Spreadsheet) |
hw1: neural networks
assigned Interim report guidelines ( pdf file ) Final report guidelines ( pdf file ) |
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| 2 | M, 01/20 | no class -- Martin Luther King, Jr. Day recess | |||
| W, 01/22 | Ch. 18, Sections 6,7: Neural Networks (cont'd) | ||||
| F, 01/24 | Ch. 18, Sections 6,7: Neural Networks (cont'd) | ||||
| 3 | M, 01/27 | Ch. 3, Sections 1 -- 4: Search, Uninformed Search | ch03-a.pdf | Homework 1 interim report is due | |
| W, 01/29 | Ch. 3, Sections 1 -- 4: Search, Uninformed Search (cont'd) | ||||
| F, 01/31 | Ch. 3, Sections 1 -- 4: Search, Uninformed Search (cont'd) | ||||
| 4 | M, 02/03 | Ch. 3, Sections 5 -- 6: Informed (Heuristic) Search | ch03-b.pdf | ||
| W, 02/05 | Ch. 3, Sections 5 -- 6: Informed (Heuristic) Search (cont'd) |
hw2: search
assigned | Homework 1 is due | ||
| F, 02/07 | no class -- Winter Carnival | ||||
| 5 | M, 02/10 | Ch. 4, Section 1: Local Search Algorithms and Optimization Problems | ch04.pdf | ||
| W, 02/12 | Ch. 5: Adversarial Search | ch05.pdf | Homework 2 is due | ||
| F, 02/14 | |||||
| 6 | M, 02/17 |
(Note: the MTU Career Fair is tomorrow, on Tuesday, 2/18.)
Hui Meen's presentation on using genetic algorithms for optimizing space trajectories |
hw3: alpha-beta pruning | ||
| W, 02/19 | Wrap up Chapters 4 and 5: Ch. 4, Section 1: Local Search Algorithms and Optimization Problems Ch. 5: Adversarial Search Example board games |
ch05-examples.pdf | Homework 3 is due | ||
| R, 2/20 | Exam 1 Time: 6:00pm-7:30pm Place: Rekhi 214 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 The sections that are part of chapters 18, 3, and 4, but are not listed above will not be included in the exam. |
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| F, 02/21 | no class-- Michigan Tech closed at 2:00pm due to weather | ||||
| 7 | M, 02/24 | Exam 1 moved to today due to inclement weather on Thursday and Friday | |||
| W, 02/26 | Ch. 07 - Logical Agents | aima-ch07.pdf | |||
| F, 02/28 | Ch. 07 - Logical Agents (cont'd)
Ch. 08 - First-Order Logic |
aima-ch08.pdf | |||
| 8 | M, 03/03 | Ch. 08 - First-Order Logic (cont'd)
Ch. 09 - Inference in First-Order Logic |
ch09.pdf | ||
| W, 03/05 |
hw4: logic
assigned | ||||
| F, 03/07 | |||||
| -- | M, 03/10 | no class -- Spring recess | |||
| W, 03/12 | no class -- Spring recess | ||||
| F, 03/14 | no class -- Spring recess | ||||
| 9 | M, 03/17 | Ch. 18, Section 3: Learning Decision Trees | ch18-03.pdf | ||
| W, 03/19 | Ch. 18, Section 3: Learning Decision Trees (cont'd) | ||||
| F, 03/21 |
Ch. 19, Section 1: Knowledge in Learning Version space learning |
ch19-01.pdf | |||
| 10 | M, 03/24 |
Ch. 18, supplemental Clustering |
clustering.pdf | Homework 4 is due | |
| W, 03/26 | |||||
| R, 3/27 | Exam 2 Time: 6:00pm-7:30pm Place: Rekhi 214 Topics covered (textbook and slides): Chapter 7 Logical Agents Chapter 8 First-Order Logic Chapter 9 Inference in First-Order Logic |
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| F, 03/28 | |||||
| 11 | M, 03/31 | Ch. 12, Section 6: Dealing with default information | ch12-06.pdf | ||
| W, 04/02 |
Ch. 13: Quantifying uncertainty
Probabilistic reasoning |
ch13.pdf | |||
| F, 04/04 | hw5: uncertainty
( pdf file ) |
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| 12 | M, 04/07 | ||||
| W, 04/09 | Ch. 10: Classical planning | ch10.pdf | |||
| F, 04/11 | hw6: decision trees
( pdf file ) |
Homework 5 is due | |||
| 13 | M, 04/14 | ||||
| W, 04/16 | |||||
| F, 04/18 | |||||
| 14 | M, 04/21 | ||||
| W, 04/23 | |||||
| F, 04/25 | Homework 6 is due | ||||
| F | M, 04/28 | Final Exam Time: 5:30pm-7:30pm Place: EERC 100 Topics covered (textbook and slides): 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 14.7.3 Representing vagueness: Fuzzy sets and fuzzy logic Ch. 13 - Uncertainty Ch. 14 - Probabilistic Reasoning Bayesian Belief Networks Ch. 10 - Classical Planning |