Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
 CSE 473 - Introduction to Artificial Intelligence - Spring 2000
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Instructor: Pedro Domingos
Office: Sieg 216
Office hours: Wednesdays 10:30 - 11:30 and by appointment 		TA: Cody Kwok
Office: Sieg 226A
Office hours: Mondays 12:30 - 1:30 and by appointment 		TA2: Yongshao Ruan
Office: Sieg 226B
Office hours: Thurs 11:30 - 12:20 and by appointment

Class meets:
MWF 11:30-12:20 LOW 105

Final:
June 7th, Wed 2:30-4:30 LOW 105
Closed book, covers everything

Topics

Assignments

There will be two homeworks, two projects, and a final exam. You are expected to work individually on the homeworks, and in groups of two on the projects.

Schedule Assignment % of Grade Topic Additional Info
Week 2-3 Homework 1 5 Search MS Word version of the homework, FAQ, Best-first search, solution
Week 3-6 Project 1 25 Satisfiability solvers Current Groups, FAQ, Hints, C/C++ resources, Looking for a partner?
Week 6-7 Homework 2 10 Reasoning & uncertainty Solutions
Week 7-10 Project 2 25 Ensemble learners  
  Final 35   Practice exam:Postscript version|PDF/Acrobat version, Review topics


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Textbooks

Papers

Readings

Week 1: Chapters 1 and 3 of Russell & Norvig
Week 2: Chapter 4 of Russell & Norvig and SAT solvers section of Weld paper
Week 3: Chapters 5 and 6 of Russell & Norvig
Week 4: Chapters 7 and 9 of Russell & Norvig
Weeks 5 & 6: Chapters 14, 15 and 16 of Russell & Norvig; review probability and statistics
Week 7: Chapters 1, 2 and 3 of Mitchell
Week 8: Ensembles section of Dietterich paper and Chapter 8 of Mitchell
Week 9: Chapters 6 and 4 of Mitchell

Lecture Notes

Week 1: Introduction, uninformed search
Week 2: Informed search, constraint satisfaction, satisfiability
Week 3: Game playing, propositional logic
Week 4: First-order logic (Part 1, Part 2, Part 3)
Weeks 5 & 6: Uncertainty (Part 1, Part 2, Part 3, Part 4, Part 5)
Week 7: Intro to machine learning, version spaces, decision trees (Part 1, Part 2)
Week 8: Learning ensembles, instance-based learning
Week 9: Bayesian learning (Part 1, Part 2), neural networks (Part 1, Part 2)
Week 10: Planning (Part 1, Part 2)

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University of Washington
Box 352350
Seattle, WA  98195-2350
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