CSCE 582 {=STAT 582} Bayesian Networks and Decision Graphs

Prerequisites: CSCE 350 (formerly CSCI 220) (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: TTh 0930-1100, SWGN 2A15
Instructor: Marco Valtorta
Office: Swearingen 3A55, 777-4641
Office Hours: TTh 1045-1215 or by previous appointment,
Teaching Assistant: TBD

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.

The material in this course is very new. It is not very difficult, but it is likely that you have never seen anything quite like this in your academic career. Therefore, it is very important that you do the homework as it is assigned. Homework is due at the beginning of class. Homework submitted after the beginning of class but before the beginning of the next class is assessed a 20% penalty. Homework submitted after that but before the beginning of the next class is assessed an additional 10% penalty per day. A grade of zero is assigned to homework that is more than a week late. It is essential for your understanding of the subject matter that you do the homework on your own. Undoubtedly, you can find solutions to some of the exercises on the web or elsewhere. If you find the solution to some exercise in the literature, please indicate clearly the source (book, article, conference paper, web site) you consulted. I will then discuss with you the situation. I will not penalize you (and may even commend you for your scholarship), but I will make sure that you understand the concepts involved in the assigned exercise. Since some of the homework involves drawing graphs, you may find it easier to turn in handwritten homework. This is acceptable, but you may be penalized for homework that is not clearly and neatly written.

Honor code violations will be followed up as required by the Academic Responsibility section of "Carolina Community: USC Student Handbook and Policy Guide." (You may want to review this document. In particular note that the instructor's discretion is quite limited in cases involving academic dishonesty.) Honor code violations will impact your grade negatively.

Students are expected to be aware of the university policy on use of computing resources, including the Student Guidelines for Responsible Computing, as well as the college and departmental policies on proper use of computing resources. Every instance of a suspected violation will be reported.

Grading Policy

Syllabus and Required Text

Lecture Log

Lecture Notes

Notes on NSDP (typed by Mr. Blaine Nelson, MS-Word format)

Homework grading policy, per assignment

Useful Links (to be constructed)

Midterm Test from Fall 2000 Edition of CSCE 590 (in postscript, with answer) (in postscript, two pages per sheet, with answer) Note that the table in the answer to 6.2 has rows and columns interchanged with respect to the convention we normally follow in this course.
Some sample questions for Midterm 2 (in postscript)