Entropie und Informationstheorie [MA601185]

Wintersemester 2013/14

Prof. Dr. Michael M. Wolf

Dozent:	Prof. Dr. Michael M. Wolf
Übungsleitung:
Mitwirkende:
Time and location:	Monday 16:15-17:45, room 03.10.011	Anmeldung
Zentralübung:		Anmeldung
Tutorübungen:		Anmeldung

News

• 22.10.2003, The reading list has been sent out.
• 14.10.2013, The five topics have been assigned and the presentation dates have been fixed. Also, we set up rules of this course. For those updates, please see below.
• 25.09.2013, The time and location of our seminar has been confirmed (see above). Our first meeting will be on 14.10.2013.
• 19.09.2013, We meet up during the first week and distribute seminar topics to the participants. More details on this course, including the time slots and rooms, are to be announced shortly.

Content

Through this course we learn the basics of classical information theory. The list of topics is:

1. Basic entropy & Information inequalities, Ms Lambacher,02 December.
2. Stochastic processes, data processing & entropy rates, Ms Schleibner, 09 December.
3. Data compression & Shannon's source coding theorem, Mr Koller, 16 December.
4. Shannon's noisy channel coding theorem, Mr Bader, 13 January.
5. Axiomatic approaches to entropic quantities, Mr Lindlacher, 20 January.
6. Von Neumann entropy and Quantum data compression, not assigned.

Rule

1. Each presentation should be 60-70 minutes.
2. Presentations should be given on the blackboard. However, one can use a projector for specific reasons, for example, showing pictures or graphs.
3. The aim of presentations is to teach other students.
4. A summary (2 pages) of the assigned topic should be submitted which contains, for example, definitions and references.
5. One should have (at least) two meetings with Dr Fukuda before the presentation. The first meeting is for deciding what to do, and the second for checking if one is ready for the talk.

Literature

1. Thomas M. Cover and Joy A. Thomas, "Elements of Information Theory", Wiley-Interscience; 2 edition (2006).
2. David MacKay, "Information Theory, Inference, and Learning Algorithms",Cambridge University Press; First Edition edition (2003), FREE HERE .
3. Claude E. Shannon, "A Mathematical Theory of Communication", Bell System Tech. J. 27, (1948). 379–423, 623–656. PDF file.