Information Measures: Part 1

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Explores mutual information in biological data, emphasizing its role in quantifying statistical dependence and analyzing protein sequences.

Maximal Correlation: Information Measures

Explores maximal correlation in information theory, mutual information properties, Renyi's measures, and mathematical foundations of information theory.

Probability Distribution and Entropy

Explains probability distribution, entropy, and Gibbs free entropy, along with the Weiss model.

Data Compression: Entropy Definition

Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Random Variables and Information Theory Concepts

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

Information Measures

Covers variational representation and information measures such as entropy and mutual information.

Source Coding Theorems: Entropy and Source Models

Covers source coding theorems, entropy, and various source models in information theory.

Information Theory: Quantifying Messages and Source Entropy

Covers quantifying information in messages, source entropy, common information, and communication channel capacity.