Information Theory: Channel Capacity and Convex Functions

Related lectures (30)

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Explores entropy, uncertainty, coding theory, and data compression applications.

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

Discusses conditional entropy and its role in data compression techniques.

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Covers the fundamentals of information theory, focusing on source coding and channel coding.

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

Explains how entropy measures uncertainty in a system based on possible outcomes.

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

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.