Lectures related to Information Theory: Basics and Applications

Covers the demonstration of Shannon's theorem, focusing on data compression.

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

Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.

Introduces data compression, entropy, and the importance of reducing redundancy in data.

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.

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

Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.

Discusses conditional entropy and its role in data compression techniques.