Data Compression and Entropy Interpretation

This lecture covers the origins of entropy in physics by Boltzmann and its interpretation as a measure of disorder in a physical system. It also delves into Shannon's theory of information, explaining how entropy quantifies the amount of information in a signal. The lecture further explores the general definitions of entropy, emphasizing the role of probabilities in calculating entropy and the complexity involved in determining entropy for real sequences. The concept of entropy is illustrated through examples, highlighting how the number of different letters in a sequence relates to disorder, novelty, and information content. Additionally, the lecture discusses how the presence of similar letters reduces disorder, leading to redundancy and less information in the message.