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Information Theory [MA5103]

Wintersemester 2012/13

Prof. Dr. Michael M. Wolf

Dozent: Prof. Dr. Michael M. Wolf
Vorlesung: Fr. 08:30 - 10:00, Raum 00.07.011 Anmeldung
Zentralübung: Mo. 14:15 - 15:45, Raum 00.07.011 Anmeldung
Tutorübungen:   Anmeldung


file version remarks
Homework 1 19.10.12  
Homework 2 14.11.12 typos corrected.
Homework 3 14.11.12  
Homework 4 30.11.12 The capacity formula is added.
Homework 5 14.12.12  
Homework 6 21.01.13 errors corrected.
Homework 7 25.01.13  


The course introduces the basics of classical information theory. This includes entropies and their properties, the asymptotic equipartition property, data compression, coding theory, channel capacities and an information theoretic perspective on statistics and data analysis.

file version content
Lecture 1 19.10.12 Intro, preliminaries on prob.theory, convexity and entropic quantities
Lecture 2 26.10.12 Entropy inequalities, mutual info, conditional entropy, Venn diagrams, perfect crypto systems, chain rules
Lecture 3 05.11.12 Data processing inequality, Fano's inequality, entropy rates
Lecture 4 09.11.12 Data compression, symbol codes, code trees for prefix codes, Kraft's inequality, entropy (rate) bounds
Lecture 5 16.11.12 Huffman codes, arithmetic coding, Lempel-Ziv coding
Lecture 6 23.11.12 lossy compression, AEP, typical sets, Shannon's source coding theorem
Lecture 7 30.11.12 discrete memoryless channels, joint AEP, rates, capacities
Lecture 8 7.12.12 Shannon's noisy channel coding theorem
Lecture 9 14.12.12 Properties of the capacity, codes with feedback
Lecture 10 21.12.12 source-channel separation, error correcting codes
Lecture 11 11.01.13 linear codes, Hamming bound, Gilbert-Varshamov bound
Lecture 12 18.01.13 Singleton bound, MDS codes, Reed-Solomon codes
Lecture 13 28.01.13 Decoding Reed-Solomon codes, a reference is: R.J. McEliece "The Decoding of Reed-Solomon Code" Pfeil
Lecture 14 1.02.13 Signal recovery via L1 and L2 uncertainty relations, Logan's phenomenon


  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 Pfeil.
  3. Claude E. Shannon, "A Mathematical Theory of Communication", Bell System Tech. J. 27, (1948). 379–423, 623–656. PDF file. Pfeil