Conditional Entropy: Review and Definitions

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Information Theory Basics

Introduces information theory basics, including entropy, independence, and binary entropy function.

Thermodynamic Identity: Entropy and Energy

Explores the thermodynamic identity, entropy-temperature relationship, and pressure definition, illustrating key principles with practical examples.

Information Theory: Entropy and Capacity

Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.

Entropy and the Second Law of Thermodynamics

Covers entropy, its definition, and its implications in thermodynamics.

Mutual Information: Understanding Random Variables

Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.

Source Coding Theorem

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Information Measures

Covers information measures like entropy and Kullback-Leibler divergence.

Thermodynamics: Entropy and Ideal Gases

Explores entropy, ideal gases, and TDS equations in thermodynamics, emphasizing the importance of the Clausius inequality and the Carnot cycle.

Information Measures

Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.

Conditional Entropy: Huffman Coding

Explores conditional entropy and Huffman coding for efficient data compression techniques.