The expected learning outcomes of this class are:

- Students will be able to explain the fundamentals of probability theory and graph theory, and apply relevant concepts to describe, model, and analyze data sets.
- Students will be able to present analyze and model data sets by applying knowledge from topics including probability distributions, Bayes theorem, conditional independence, discrete and continuous models, regression models, hypothesis testing, and Markov chain methods. If time permits, time series analysis and ARMA models will also be discussed.

The class will require graduate standing and preferably have taken CE5690 and/or CE5390. In addition, students are expected to come with an

The class meets thrice every week:

Lectures: ??

Office hours:

Amlan Mukherjee 2007-09-03