At the completion of this course, the student should be able to: demonstrate knowledge and understanding of the fundamentals of information theory; appreciate the notion of fundamental limits in communication systems and more generally all systems; develop deeper understanding of communication systems; apply the concepts of information theory to various disciplines in information science.

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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.

Syllabus

WEEK 1: Course Preliminaries

WEEK 2: Chapter 2 Information Measures - Part 2

WEEK 3: Chapter 3 The I-Measure - Part 1

WEEK 4: Chapter 3 The I-Measure - Part 2, Chapter 4 Zero-Error Data Compression - Part 1

WEEK 5: Chapter 4 Zero-Error Data Compression - Part 2, Chapter 5 Weak Typicality

WEEK 6: Chapter 6 Strong Typicality

WEEK 7: Chapter 7 Discrete Memoryless Channels - Part 1

WEEK 8: Chapter 7 Discrete Memoryless Channels - Part 2

WEEK 9: Chapter 8 Rate-Distortion Theory - Part 1

WEEK 10: Chapter 8 Rate-Distortion Theory - Part 2, Chapter 9 The Blahut-Arimoto Algorithms - Part 1

WEEK 11: Chapter 9 The Blahut-Arimoto Algorithms - Part 2, Chapter 10 Differential Entropy - Part 1

WEEK 12: Chapter 10 Differential Entropy - Part 2

WEEK 13: Chapter 11 Continuous-Valued Channels - Part 1

WEEK 14: Chapter 11 Continuous-Valued Channels - Part 2

WEEK 15: Chapter 11 Continuous-Valued Channels - Part 3