Prerequisites: CSCE 350 (Data Structures and Algorithms) and STAT 509 (Statistics for Engineers)
Bulletin Description: Bayesian Networks and Decision Graphs. {=STAT 582} (3) (Prereq: CSCE 350 and STAT 509) Normative approaches to uncertainty in Artificial Intelligence. Probabilistic and causal modeling with Bayesian networks and influence diagrams. Applications in decision analysis and support. Algorithms for probability update in graphical models.
Meeting Time and Place: MWF 1325-1415, SWGN 2A27
Instructor: Marco Valtorta
Office: Swearingen 3A55, 777-4641
E-mail:
mgv@cse.sc.edu
Office Hours:
MWF 1100-1200. Please check by phone or email. Others by
appointment.
The goals of this course are:
The course is foundational. It concentrates on modeling and use of decision analysis principles. Algorithms for belief updating (especially variable elimination and Jensen's version of the Lauritzen-Spiegelhalter algorithm, but also stochastic simulation) are discussed to some depth, but advanced topics on algorithmic issues are left out. It is my hope that a student who successfully completes this course will both be able to use decision analytic tools such as Hugin well and be well prepared for advanced graduate courses in, e.g., data mining.
Students are expected to be aware of the Network Guidelines for Responsible Computing, as well as the College of Engineering and Computing policies on proper use of computing resources.
Graduate Student Presentations
Homework