Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
Discusses mutual information, data processing inequality, and properties related to leakage in discrete systems.
Covers variational representation and information measures such as entropy and mutual information.
