Lectures related to Data Compression and Shannon's Theorem: Huffman Codes

Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.

Explores Shannon's theorem on data compression and the performance of Shannon Fano codes.

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

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

Explores conditional entropy and Huffman coding for efficient data compression techniques.

Explores entropy as a 'question game' to guess letters efficiently and its relation to data compression.

Introduces Shannon's Theorem on binary codes, entropy, and data compression limits.