Course Material
Part 1: Fundamentals
Lecture 1: Introduction to Probability and Information Measures
Lecture 2: Information Theory in the communications chain
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Part 2: Source
Lecture 3: Inequalities and Source Coding
Lecture 4: Huffman Coding, SFE codes, Lempel-Ziv codes
Lecture 5: Asymptotic Equipartition Property (AEP)
Lecture 6: AEP and Source Coding theorem
Lecture 7: Markov Sources and Entropy Rate
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Part 3: Communication Channel
Lecture 8: Channel Capacity
Lecture 9: Channel Capacity of Discrete channels: BSC, BEC
Lecture 10: Convolutional channel codes, Turbo Codes, Viterbi decoder
Lecture 11: Channel coding theorem
Lecture 12: Capacity of the Gaussian channel
Lecture 13: Parallel Gaussian Channels and the Waterfilling
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Part 4: Special topics
Lecture 14: Physical Layer security Concepts
Lecture 15: Advanced topics on information theory
Lecture 16: Advanced topics on information theory
Lecture 17: Advanced topics on information theory
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Instructor: Prof. Dr.-Ing. Samah A. M. Ghanem
Assessment Criteria: Midterm 25%, Assignments 25%, Final 50%
Textbook: Elements of Information Theory, by Joy A. Thomas and Thomas M. Cover, Wiley