Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
