Course 32 Information Theory
Information Theory is a fundamental area of study that explores the quantification, storage, and communication of information. It provides a mathematical framework for understanding how information can be measured, encoded, transmitted, and decoded efficiently, often under conditions of uncertainty and noise. Developed in the mid-20th century by Claude Shannon, the field has had profound implications for various disciplines, including data science, telecommunications, cryptography, and machine learning. At its core, Information Theory examines concepts such as entropy, mutual information, and channel capacity, which are the foundation for modern communication systems and algorithms. Its applications continue to shape how we process and transmit information in the digital age.
https://en.wikipedia.org/wiki/Similarity_measure https://en.wikipedia.org/wiki/Edit_distance https://en.wikipedia.org/wiki/Hamming_distance https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm https://en.wikipedia.org/wiki/Index_of_dissimilarity https://en.wikipedia.org/wiki/Isolation_index