Lectures related to Conditional Entropy: Huffman Coding

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

Covers the concepts of compression and prediction using prefix-free codes and distributions.

Explores the relationship between code word length and probability distribution, focusing on designing prefix-free codes for efficient compression.

Explores the origins and interpretation of entropy, emphasizing its role in measuring disorder and information content in a system.

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

Explores compression in stochastic processes through injective codes and prefix-free codes.