Lectures related to Source Coding and Prefix-Free Codes

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

Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.

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

Explores mutual information and entropy calculation between random variables.

Covers prefix-free codes, Kraft inequality, Huffman coding, and entropy.

Explores the performance of Shannon-Fano algorithm and introduces Huffman codes for efficient data compression.

Explores entropy in information theory and its role in data processing and probability distributions.

Covers error correction codes theory and applications, emphasizing the importance of minimizing distance for reliable communication.