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Unformatted text preview: CSCI 571 Artificial Intelligence, Section 1230 Summer 2010 Prerequisite: None Administrative Information: Class Duration: July 5 to Sep 29 Annandale Campus Phone: (703) 941-0949 Day/Time: Saturday 9-1pm Location: Virtual class Instructor Contact Information: Instructor: Raied Salman Office Hours: (Annandale) Thursday, 2-6pm, Friday 10-2pm. Other appointments by email Office: Annandale Tel.: 703-941-0949 ext 131 E-mail: [email protected] Instructional Material: Artificial Intelligence: A Modern Approach Edition: 2nd Author(s): Russell, Stuart; Norvig, Peter Course Description: CSCI 571 is a graduate-level introduction to artificial intelligence. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning. 1 Course Learning Outcome: Students will be introduced to the basic concepts and techniques of Artificial Intelligence. They will learn AI by developing skills of using AI algorithms for solving practical problems. Upon successfully completing this course the student will be able to • To gain experience of doing study and research. • Demonstrate the appropriate level of competence in written expression as demanded by the discipline and as expected of a graduate student. • Demonstrate the appropriate level of competence in library research as demanded by the discipline and as expected of a graduate student Teaching Method: The class format will include readings, multimedia based presentations, and case discussion. Small development projects will be included for both individual and team work. Significant class time will be used to discuss, explore and analyze recent developments and technologies. This course emphasizes the importance of relating the gained knowledge to real world applications so practical development projects will be an integral part of the course. These practical projects will be part of each unit; this allows continuous integration of theory and practice. This class is taught as a lecture, with demonstrations of key processes and homework that require students to use the programs and processes so that they are practicing their skills outside the classroom. This class does not include a formal laboratory component....
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This document was uploaded on 02/13/2011.
- Spring '11
- Artificial Intelligence