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Course Description:
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This course covers the fundamentals of artificial
intelligence. The course can be taken as a part of general CS education, as a
first step to further study in AI, or to gain familiarity with AI
methods for applications in other fields.
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Textbook: |
Artificial Intelligence: A Modern Approach,
Stuart Russell and Peter Novig, Prentice Hall, 2nd Edition, 2003 (ISBN
0-13-790395-2)
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References: |
ANSI Common LISP, Paul Graham, Prentice
Hall,
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Instructor: |
Dr. Shi Zhong, Assistant Professor of Computer
Science and Engineering
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Contact: |
zhong@cse.fau.edu, 7-3168, S&E 366
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Goals: |
To learn the fundamentals of various areas of
artificial intelligence; To understand search as the fundamental problem
solving technique; To know basic concepts and methods in logic
inference; To learn probabilistic uncertainty reasoning; To learn inductive learning algorithms and advanced
machine learning techniques.
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Time and Place: |
MWF, 10:00-10:50AM, GS 111
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Office Hours: |
MW, 3:30-6:30pm (other times by appointment only) |
Prerequisites:
No prerequisites are needed other than some programming experience
with a high level language and some exposure to formal methods (e.g., logic,
discrete maths).
Topics:
- Problem solving, Search
- Knowledge representation, Logic inference
- Probabilistic reasoning, Bayesian networks
- Machine learning, Robotics
Be sure to check out
the textbook website,
which contains enormous information on this course and in general, all
topics on AI.
Course grading:
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40% homework assignments (20%/20%, 2 assignments)
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40% exam (15% midterm in class, Monday, Oct 10, 2005;
25% final exam, time/place TBD)
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20% group project (due Monday midnight, November 21,
2005)
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10% optional in-class presentation (bonus points, 11/28
or 11/30)
Policy on cheating:
Any form of cheating will not be tolerated. You are
allowed to discuss with others on your homework assignments but cannot copy
programs or any writing from others.
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