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252-0055-00L 4 Credits BSC D-INFK
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Information Theory

Informationstheorie

Lecturers & Examiners: Prof. em. Dr. Joachim M. Buhmann
VVZ CR 3.5

Last Updated: 2026-02-05 15:54:24

Abstract

The course covers the fundamental concepts of Shannon's information theory.The most important topics are: Entropy, information, data compression, channel coding, codes.

Objective

The goal of the course is to familiarize with the theoretical fundamentals of information theory and to illustrate the practical use of the theory with the help of selected examples of data compression and coding.

Content

Introduction and motivation, basics of probability theory, entropy and information, Kraft inequality, bounds on expected length of source codes, Huffman coding, asymptotic equipartition property and typical sequences, Shannon's source coding theorem, channel capacity and channel coding, Shannon's noisy channel coding theorem, examples

Resources

Literature

T. Cover, J. Thomas: Elements of Information Theory, John Wiley, 1991. D. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003. C. Shannon, The Mathematical Theory of Communication, 1948.

Learning Materials (Links)

General Information

Language
German
Levels
BSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Keine!
Prüfung wird letztmalig in der Wintersession 2022/23 angebotenThe exam will be offered for the last time in the Winter examination session 2022/23

Course Components

Type Title Time & Place Hours
lecture Informationstheorie
  • Thu 14:15-16:00 (ML F 36)
2 h weekly
exercise Informationstheorie
  • Wed 16:15-18:00 (HG D 7.1)
1 h weekly

Offered In

  • Computer Science Bachelor
    • Electives (Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.)