Shannon's Information Theory

Shannon's Information Theory is a mathematical framework for understanding how information is measured, transmitted, and processed. It quantifies information based on its unpredictability or surprise—more surprising messages contain more information. The theory introduces concepts like entropy, which measures the average information content in a source, and data compression, which reduces redundancy to transmit data efficiently. It also defines the limits of reliable communication over noisy channels, helping to design systems that can accurately decode messages even with errors. Overall, Shannon's theory provides a foundation for digital communication, data compression, and cybersecurity.