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Both these concepts transcend mere practicality; they play a fundamental role in numerous machine-learning algorithms, especially in unsupervised learning scenarios. Furthermore, information theory equips us with robust analytical tools to compare probability distributions, which is vital for various probabilistic modeling approaches.
Claude Shannon and the Birth of Information Theory
The story of information theory finds its roots with Claude Shannon, who, in 1948, published a seminal paper titled "A Mathematical Theory of Communication." This work not only introduced information theory but also solved key problems within the domain, introducing a method to quantify the information content corresponding to outcomes of random variables.