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Coursera

Information Theory

The Chinese University of Hong Kong via Coursera

Overview

The lectures of this course are based on the first 11 chapters of Prof. Raymond Yeung’s textbook entitled Information Theory and Network Coding (Springer 2008). This book and its predecessor, A First Course in Information Theory (Kluwer 2002, essentially the first edition of the 2008 book), have been adopted by over 60 universities around the world as either a textbook or reference text.

At the completion of this course, the student should be able to:
1) Demonstrate knowledge and understanding of the fundamentals of information theory.
2) Appreciate the notion of fundamental limits in communication systems and more generally all systems.
3) Develop deeper understanding of communication systems.
4) Apply the concepts of information theory to various disciplines in information science.

Syllabus

  • Course Preliminaries
  • Chapter 1 Information Measures
  • Chapter 2 Information Measures - Part 1
  • Chapter 2 Information Measures - Part 2
  • Chapter 3 The I-Measure - Part 1
  • Chapter 3 The I-Measure - Part 2
  • Chapter 4 Zero-Error Data Compression - Part 1
  • Chapter 4 Zero-Error Data Compression - Part 2
  • Chapter 5 Weak Typicality
  • Chapter 6 Strong Typicality
  • Chapter 7 Discrete Memoryless Channels - Part 1
  • Chapter 7 Discrete Memoryless Channels - Part 2
  • Chapter 8 Rate-Distortion Theory - Part 1
  • Chapter 8 Rate-Distortion Theory - Part 2
  • Chapter 9 The Blahut-Arimoto Algorithms - Part 1
  • Chapter 9 The Blahut-Arimoto Algorithms - Part 2
  • Chapter 10 Differential Entropy - Part 1
  • Chapter 10 Differential Entropy - Part 2
  • Chapter 11 Continuous-Valued Channels - Part 1
  • Chapter 11 Continuous-Valued Channels - Part 2
  • Chapter 11 Continuous-Valued Channels - Part 3

Taught by

Raymond W. Yeung

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