| W | Date | Topic | Slides | Assign | Collect |
| 1 | T, 1/13 |
Brief course overview Brief introduction to AI |
ch01.pdf
ch01.ppt |
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| R, 1/15 |
Ch. 20, Section 5: Neural Networks
Introduction Training with examples |
ch20-01.pdf
ch20-01.ppt ch20-02.pdf ch20-02.ppt ch20-03.pdf aima-ch20b.pdf aima-ch20b-6pp.pdf |
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| 2 | T, 1/20 |
Ch. 20, Section 5: Neural Networks
The error correction procedure Neural network training algorithms |
ch20-04.pdf
ch20-05.pdf |
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| R, 1/22 |
hw1: neural networks
assigned |
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| 3 | T, 1/27 | Ch. 03 - Problem Solving by Searching |
Ch. 03 slides
full page pdf, ps, 4 per page ps |
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| R, 1/29 | Homework 1 interim report | ||||
| 4 | T, 2/03 | In class exercise pdf | |||
| R, 2/05 | No class (Winter Carnival) | ||||
| 5 | T, 2/10 | Ch. 04 - Informed Search and Exploration |
Ch. 04 slides
full page pdf, ps, 4 per page ps |
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| R, 2/12 | CH. 06 - Game Playing |
ch06a.pdf
ch06a.ppt ch06b.pdf ch06b.ppt |
Homework 1 will be collected on Friday |
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| 6 | T, 2/17 |
hw2: search
assigned |
|||
| R, 2/19 | Ch. 07 - Logical Agents |
aima-ch07.pdf aima-ch07-6pp.pdf |
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| 7 | T, 2/24 |
hw2: search
will be collected |
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| R, 2/26 |
Exam 1 Topics covered: Chapter 20 Statistical Learning Models 20.5 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 Avoiding Repeated States Chapter 4 Informed Search and Exploration 4.1 Informed (Heuristic) Search Strategies 4.2 Heuristic Functions 4.3 Local Search Algorithms and Optimization Problems Chapter 6 Adversarial Search 6.1 Games 6.2 Optimal Decisions in Games 6.3 Alpha-Beta Pruning Topics NOT covered: Chapter 3 Solving Problems by Searching 3.6 Searching with Partial Information Chapter 4 Informed Search and Exploration IDA* and memory bounded heuristic search 4.4 Local Search in Continuous Spaces 4.5 Online Search Agents and Unknown Environments Chapter 6 Adversarial Search 6.4. Imperfect, Real-Time Decisions 6.5. Games that Include an Element of Chance 6.6. State of the Art Game Programs |
Good luck! | |||
| 8 | T, 3/03 | Ch. 08 - First-Order Logic |
aima-ch08.pdf aima-ch08-6pp.pdf |
hw3: propositional logic
assigned |
|
| R, 3/05 | Ch. 09 - Inference in First-Order Logic |
aima-ch09.pdf aima-ch09-6pp.pdf |
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| - | T, 3/10 | No class (Spring Break) | |||
| R, 3/12 | No class (Spring Break) | ||||
| 9 | T, 3/17 |
hw4: First order logic
assigned |
hw3: propositional logic
will be collected |
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| R, 3/19 | Ch. 18 - Learning from Observations |
aima-ch18.pdf aima-ch18-6pp.pdf dt-examples.ppt dt-examples.pdf |
|||
| 10 | T, 3/24 |
Ch. 19 - Knowledge in Learning Version spaces |
vs-examples.ppt vs-examples.pdf |
hw5: Decision tree learning
assigned |
hw4: First order logic
will be collected |
| R, 3/26 |
Ch. 23 - Probabilistic Language Processing Presentation of result sets (clustering) |
clustering-examples.ppt
clustering-examples.pdf |
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| 11 | T, 3/31 | ||||
| R, 4/02 |
Exam 2 Topics covered: Chapter 7 Logical Agents 7.1 Knowledge-Based Agents 7.2 The Wumpus World 7.3 Logic 7.4 Propositional Logic: A Very Simple Logic 7.5 Reasoning Patterns in Propositional Logic Chapter 8 First-Order Logic 8.1 Representation Revisited 8.2 Syntax and Semantics of First-Order Logic 8.3 Using First-Order Logics Chapter 9 Inference in First-Order Logic 9.1 Propositional vs. First-Order Inference 9.2 Unification and Lifting 9.5 Resolution |
Good luck! | |||
| 12 | T, 4/07 |
Ch. 13 - Uncertainty Qualitative Reasoning |
uncertainty.ppt
uncertainty.pdf |
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| R, 4/09 |
Ch. 13 - Uncertainty Probabilistic Reasoning |
probabilistic.ppt
probabilistic.pdf |
hw6: machine learning, uncertainty
assigned |
hw5: Decision tree learning
will be collected |
|
| 13 | T, 4/14 | Ch. 11 - Planning |
planning.ppt
planning.pdf |
||
| R, 4/16 | Research topics: Applications of planning | Grades so far |
hw7: planning
assigned |
hw6: machine learning, uncertainty
will be collected |
|
| 14 | T, 4/21 |
Research topics: Web Services, the Semantic Web Research topics: A potpourri of planning and scheduling research projects at Michigan Tech |
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| R, 4/23 |
Research topics: A potpourri of planning and scheduling research
projects at Michigan Tech Research topics: The CYC project (by Doug Lenat for Google) Link to Google Video |
hw7: planning
will be collected |
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| F | R, 04/30 | Final Exam on Thursday at 10:15am Place: Fisher 231 (the regular classroom) ( OSSR's schedule) Final Exam Topics covered: Chapter 18 Learning from Observations 18.1 Forms of Learning 18.2 Inductive Learning 18.3 Learning Decision Trees Ch. 19 - Knowledge in Learning 19.1 Version Space Learning Ch. 23 - Probabilistic Language Processing 23.2 Presentation of result sets (clustering) Ch. 13 - Uncertainty Ch. 14 - Probabilistic Reasoning 14.7 Representing vagueness: Fuzzy sets and fuzzy logic Ch. 11 - Planning |
Good luck! |