Lectures related to Conditional entropy

Explores mutual information and entropy calculation between random variables.

Probability Theory: Joint Marginals and Granger Causality

Covers joint marginals and Granger causality in probability theory, explaining their implications in predicting outcomes.

Entropy Bounds: Conditional Entropy Theorems

Explores entropy bounds, conditional entropy theorems, and the chain rule for entropies, illustrating their application through examples.

Chain Rule for Entropy

Explores the chain rule for entropy, decomposing uncertainty in random variables and illustrating its application with examples.

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.

Conditional Entropy: Huffman Coding

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

Sunny Rainy Source: Markov Model

Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.

Information Measures: Entropy and Information Theory

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

Information Measures

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

Conditional Entropy: Review and Definitions

Covers conditional entropy, weather conditions, function entropy, and the chain rule.

Source Coding Theorem

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

Mutual Information: Understanding Random Variables

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

Entropy and Algorithms

Explores entropy's role in coding strategies and search algorithms, showcasing its impact on information compression and data efficiency.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Conditional Entropy and Information Theory Concepts

Discusses conditional entropy and its role in information theory and data compression.

Source Coding Theorems: Entropy and Source Models

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

Information Theory: Channel Capacity and Convex Functions

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Introduction: Course Structure and Fundamentals of Computing

Explores the role of Computing in society and the basics of computing, algorithms, communication systems, and computer security.

Information Theory: Entropy and Capacity

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

Protein-Ligand Interactions: Importance and Affinity

Explores the importance of protein-ligand interactions, focusing on binding affinities and energetic landscapes, with implications for drug development and specificity.