Information Theory: Source Coding, Cryptography, Channel Coding

Related lectures (24)

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Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Introduces erasure and error channels, Hamming distance, and error correction codes.

Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.

Covers source coding theorems, entropy, and various source models in information theory.

Covers the basics of information theory and its applications in various fields.

Covers source coding, cryptography, and channel coding for communication systems.

Discusses public-key cryptography, focusing on standards like RSA, DSA, and AES, and their applications in secure communications.

Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Explains how entropy measures uncertainty in a system based on possible outcomes.