Information Theory: Source Coding & Channel Coding

Related lectures (27)

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Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Introduces information theory basics, including entropy, independence, and binary entropy function.

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Explains how to design efficient prefix-free codes for compression.

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Explores entropy, uncertainty, coding theory, and data compression applications.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.