Spring 2008 Syllabus
Logistics
| When: | 9-9:50 MWF |
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| Where: | USC310B - CS 3rd floor conference room |
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| Instructor: | Adele Howe |
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| 970-491-7589 |
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|  |
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| Office hours: TBD or any time that my door
is open |
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| Office: USC 235 |
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Description:
The course objectives are to learn techniques
and theory developed in major areas of Artificial Intelligence and to
learn about the state of the art in Artificial Intelligence. The
course will cover aspects of several major areas of Artificial
Intelligence with an emphasis on those areas in which active research
is ongoing at CSU that is not covered in other courses. The active AI
research areas in our department are: genetic algorithms, neural
networks, search, planning, scheduling, agents,
machine learning, computer vision and bioinformatics.
Other courses offered at CSU, i.e., CS510, CS545, CS580 and CS680, cover some
topics from this list. CS510 covers computer vision and advanced
graphics. CS545 covers statistical machine learning. CS540
will be designed to complement CS545 and will cover some machine
learning topics not in CS545 as part of the semester's topics. CS580
and CS680 cover bioinformatics and kernel methods.
The possible topics are: advanced search, planning, scheduling, symbolic
machine learning, genetic algorithms, data mining, agents and
evaluation. Actual topics will be determined in part from student interest.
The class is structured around a lecture format; however, class
discussions, questions and participation are strongly encouraged.
Course Requirements
Textbook:
Hoos and Stutzle's Stochastic Local Search:
Foundations and Applications
Other sources will be
course notes and recent papers.
Pre-requisites:
CS440 or equivalent. Knowledge of
fundamentals of Artificial Intelligence, search and logic.
Grading:
The course requires demonstration of student's grasp of the concepts
through exams, homework assignments and a project, as follows:
| Assignments (~4) | 40% |
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| Project | 25% |
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| Midterms (2) | 35% |
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The course will be graded on a standard 90-80-70-60 distribution for
grade levels. Exams may be curved if necessary.
See project and assignments links for descriptions of those.
- Examinations:
- Two midterms. The second midterm will be either during finals
week or the second to last week of classes based on a vote of the
class.
Logistics Related to Grading:
Each assignment must be
submitted before the beginning of class on the given deadline for that
assignment (see assignment for specifics); late period for assignments
will end at the start of the next following class and will incur a penalty
of 8%. Assignments turned in after that time receive no credit. If you
have not made prior arrangements, and you miss an exam, you will
receive a zero for the exam.
I encourage you to talk with other students about your assignments
and questions, but make sure you write your own programs and
assignments. You may not copy another student's program (either with
or without their knowledge) or write code for them. Please read the
departmental policy statement regarding incompletes, cheating, and
class attendance. This policy statement is in the file student info. We will follow
the guidelines outlined in these documents.