Lectures related to Data Compression and Shannon's Theorem: Shannon's Theorem Demonstration

Information Theory: Review and Mutual Information

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

Data Compression and Shannon's Theorem: Lossy Compression

Explores data compression, including lossless methods and the necessity of lossy compression for real numbers and signals.

Data Compression and Entropy Definition

Explores the concept of entropy as the average number of questions needed to guess a randomly chosen letter in a sequence, emphasizing its enduring relevance in information theory.

Shannon's Theorem

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

Data Compression and Entropy: Illustrating Entropy Properties

Explores entropy as a measure of disorder and how it can be increased.

Information Theory: Source Coding, Cryptography, Channel Coding

Covers source coding, cryptography, and channel coding in communication systems, exploring entropy, codes, error channels, and future related courses.