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171 Course Outline, Fall 2004
 Professor: Rina
Dechter
 Electronic Mail: dechter@ics.uci.edu
 Place: RH 101
 Time: TuTh 02:00 to 03:20p
 Office: ICS 424E
 Office Hours: Monday, Thursday, 1:00 to 2:00 pm.
 Textbooks:
 Artificial Intelligence: A Modern Approach, by Russell and Norvig.
 Classnotes
 Teaching Assistants
 Lucas Scharenbroich lscharen@uci.edu . Office hours, uesday,Thursday 1011, CSE 301.
 Andrew Felch afelch@uci.edu
Office hours are 3:304:30 Tuesday (after class) and 5:006:00 Thursday (after discussion), ETC 105
 Discussion Sections
 36471 DIS 1: MO, 10:0010:50 in ICF 101
 36472 DIS 2: Wed 02:0002:50 in ICF 101
 36473 DIS 3: Thur 4:0004:50 in ICF 101
Course Goals:
Learn the basic AI techniques, the problems for which they
are applicable and their limitations. Topics covered include
heuristic search algorithms,
Knowledgerepresentation (logicbased and probabilisticbased)
inference and learning algorithms.
Academic Honesty:
Assignments:

There will be weekly homeworks, about 78 throughout the quarter, each
on the material covered in class up to that time. Homeworks will account for 2530%
of the grade. Homeworks will be assigned on Tuesday and will be due to following Thursday at 2:00 pm in class (stay tuned for changes towards the end of the quarter)
The lowest scored homework will be dropped.
There will be no makeups for homeworks.

There will be 1 project which will
account for 1015% of the grade.

There will be one midterm exam, closed books which will account for
20% of the grade.

There will be a final exam, closed books during the final week
which will account for 40% of the grade.
Bulletin Board:
Read ics.171 for announcements, answers to homework etc.
Also, please post
questions about homework or anything else there.
If you don't understand something, others probably don't either
and will have the same question.
Procedures:
Some handouts will be distributed during the quarter by the
Distribution Center, others will be available to buy in the Engineering
Copy Center.
Syllabus:
 Lecture 1. Introduction, history, intelligent agents. Chapters 1, 2.
 Lecture 2. Problem formulation: Statespaces, search graphs,
problem spaces, problem types. Chapter 3 .
 Lectures 3. Uninformed search: breadthfirst,
depthfirst, iterative deepening, bidirectional search. Chapter 3.
 Lectures 4. Informed Heuristic search: Greedy, BestFirst, A*,
Properties of A*. Chapter 4.
 Lectures 5. Informed Heuristic search, Properties and generating heauristics, constraint satisfaction. Chapters 4,5.
 Lecture 6. Constraint satisfaction. Chapter 5.
 Lecture 7. Constraint satisfaction. Game playing Chapter 6.
 Lecture 8. Game playing. Chapter 6.
 Lecture 9. Representation and Reasoning: Propositional
logic. Chapter 7.
 Lecture 10. Representation and Reasoning: Inference in propositional
logic. Chapter 7.
 Lecture 11. Midterm
 Lecture 12. First order logic. Chapter. 9.
 Lecture 13. Inference in first order logic. Chapter 9.
 Lecture 14. Veteran day
 Lecture 15. Learning from observations. Chapter 18.
 Lecture 16. Neural networks. Chapter, 20.5
 Lecture 17. Handling uncertainty. Chapter 13
 Lecture 18. Thanksgiving
 Lecture 19. Bayesian networks. Chapter 14
 Lecture 20. Assorted topics
Resources on the Internet