HU Credits:
3
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Mathematics
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
E. Safra
Course/Module Coordinator:
Dr. Zemer Kosloff
Coordinator Office Hours:
By appointment
Teaching Staff:
Dr. Zemer Kosloff
Course/Module description:
This course is an introduction to information theory and its applications in other mathematical disciplines (measure concentration, probability theory) as well as in Physics and Engineering.
In the first part of the course we will treat the classical theory as was introduced in Shannon's seminal paper. In this part we will show how probability can be used to model some classical problems such as data compression and channel coding (fixed length and variable length) and introduce the basic concepts such as entropy, divergence and Fisher information.
The second part of the course will be concerned with applications of these methods to statistical physics, probability theory (large deviations), optimal transport and combinatorics.
The third part, time permitting will deal with differential entropy and Barron's entropic proof of the CLT.
Course/Module aims:
Gain familiarity with the field of information theory and how it connects to other mathematical disciplines.
Learning outcomes  On successful completion of this module, students should be able to:
Understand how information problems are modeled and solved using elementary mathematical tools.
Get familiar with theoretical methods which appear in information theoretical problems, in particular probabilistic methods and information calculus.
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
Lecture
Course/Module Content:
 Introduction and presentation of the basic concepts such as entropy, relative entropy, mutual information.
Method of types
Shannon's channel coding theorem;
Variable length coding and block data compression;
 Lempel Ziv algorithm.
Noisy channels
 Network Information Theory: The multiple access channels; SlepianWolf Lemma for multiple access channels.
 Large deviations
 Gibbs measures
 Optimal transport
 Stationary channels
 (Time permitting) Differential entropy and the CLT
Required Reading:

Additional Reading Material:
T M Cover and J A Thomas, Elements of information theory, Wiley interscience 1991.
R. Ash R B Ash. Information Theory, Dover Publications, 1990
Yury Polyanskiy and Yihong Wu. Lecture notes of the MIT course on information theory. Available for download at the MIT open courseware website.
O. Johnson. Information theory and the central limit theorem. Imperial College Press, London, 2004.
Course/Module evaluation:
End of year written/oral examination 60 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 40 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
