Information theory was developed to deal with the fundamental limits of communications, and it has found wide applications in areas as diverse as economics and biology. This short course covers fundamental concepts of information theory, including the mathematical description of entropy and information, noiseless source coding, noisy channel coding theorem, rate distortion theory, and introduction to some more recent topics. Useful tools such as information diagram and dependency graphs will also be introduced. Knowledge in probability and basic digital communication is assumed. Ten lectures of two hours each are planned.

 

[1] Thomas M. Cover and Joy A. Thomas, Elements of Information Theory.

[2] C. Shannon, A Mathematical Theory of Communications, 1948.

[3] Raymond Yeung, A First Course on Information Theory.

 

 

Zhengdao Wang received his B.S. degree in Electronic Engineering and Information Science from the University of Science and Technology of China (USTC), 1996, the M.Sc. degree in Electrical and Computer Engineering from the University of Virginia, 1999, and Ph.D. in Electrical and Computer Engineering from the University of Minnesota, 2002. He is now with the Department of Electrical and Computer Engineering at the Iowa State University. His interests are in the areas of signal processing, communications, and information theory. He served as an associate editor for IEEE Transactions on Vehicular Technology from April 2004 to April 2006, and has been an Associate Editor for IEEE Signal Processing Letters since August 2005. He was a co-recipient of the IEEE Signal Processing Magazine Best Paper Award in 2003 and the IEEE Communications Society Marconi Paper Prize Award in 2004, and the EURASIP Journal on Advances in Signal Processing Best Paper Award, 2008.