OR 719 - Computational Models of Probabilistic Reasoning

Institution:

George Mason University

Subject:

Description:

Credits: 3 Cross-Listed with STAT 719/CSI 775 Introduction to theory and methods for building computationally efficient software agents that reason, act, and learn environments characterized by noisy and uncertain information. Covers methods based on graphical probability and decision models. Studies approaches to representing knowledge about uncertain phenomena, and planning and acting under uncertainty. Topics include knowledge engineering, exact and approximate inference in graphical models, learning in graphical models, temporal reasoning, planning, and decision-making. Practical model-building experience provided. Students apply what they learn to a semester-long project of their own choosing. Prerequisites STAT 652 or SYST/ STAT 664, or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.gmu.edu

Phone Number:

(703) 993-1000

Regional Accreditation:

Southern Association of Colleges and Schools

Calendar System:

Semester

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