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A measure of dependence between variables, quantifying how much knowing one reduces uncertainty about the other.

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Entropy ✓ understood
A measure of uncertainty or randomness in a random variable from information theory.
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A measure of dependence between variables, quantifying how much knowing one reduces uncertainty about the other.

This concept is essential for understanding machine learning fundamentals and forms a key part of modern AI systems.

Information Theory
Dependence
Entropy

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Information Bottleneck

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Foundations KL Divergence Kullback-Leibler divergence - a measure of how one probability distribution differs from another. Foundations Information Bottleneck A principle for learning representations that compress input while retaining information relevant to prediction. Foundations Covariance A measure of how two variables change together, indicating the direction of their linear relationship. Foundations Feature Selection Choosing the most relevant features from available data to reduce dimensionality and improve model performance.