Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Explores the concept of entropy as the average number of questions needed to guess a randomly chosen letter in a sequence, emphasizing its enduring relevance in information theory.
Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Covers variational representation and information measures such as entropy and mutual information.
