Practical Probabilistic
Modeling with Graphical Models
EE 639 Advanced Topics in Signal
Processing and Communication
Fall 2009

.
8/31 
We are going to move effectively
today. Please refer to the new location and time of the lecture below. 
8/26 
Visit course website at http://www.vis.uky.edu/~cheung/courses/ee639/index.html
and our discussion group at http://groups.google.com/group/ee639 
Dr. Senching Cheung (cheung at engr.uky.edu)
Office: FPAT 687B (x79113)
Office hours: MWF 911am
Office: Room 831 VisCenter
at Kentucky Utility Building (71257 ext. 80299)
Office hours: By appointment only
Regular class: MW 3:00pm4:15pm (Relocated to the small conference room 869 at VisCenter)
Final Examination: Takehome final (will be issued on 12/14, due 12/16)
A central tenant of any empirical sciences is to construct probabilistic models for prediction and estimation based on available data. The enormous advances in computing, sensing and networking technologies provide us with an unprecedented capability in collecting and storing an inordinate amount of data. Much of these data are noisy, interrelated and highdimensional. For decades, mathematicians and engineers in different disciplines have developed specialized probabilistic models to characterize and utilize various types of data. One particular framework has gradually emerged as the most appropriate tool to unify disparate techniques and to build complex models in a modular and algorithmic fashion. This framework is the Probabilistic Graphical Model, the focus of the EE639 course this semester.
Probabilistic graphical models are probabilistic models that encode local information of conditional independency among a large number of random variables. By choosing an appropriate (sparse) graph to describe the data, powerful and rigorous techniques exist to perform prediction, estimation, datafusion as well as handling uncertainty and missing data. Many classical multivariate systems in pattern recognition, information theory, statistics and statistical mechanics are special cases of this general framework – examples include hidden Markov models, regression, mixture models, Kalman filters and Ising models. In this course, we will study how graph theory and probability can be elegantly combined under this framework to represent many commonlyused probabilistic tools, and how they can be applied in solving many practical problems. We will put equal emphasis on both theoretical understanding of the subject and practical knowhow on using it in real applications. The grading is based on three components: a number of homework assignments throughout the semester, a takehome final and a final project.
Your grade will be based on: 

Homework 
40% 
Takehome Final (due 12/16) 
20% 
Final Project (poster session and report due 12/4) 
40% 
Senching Samson Cheung
Last modified: August 16, 2009.