Concept

Interaction information

Related lectures (32)

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

Variational Formulation: Information Measures

Explores variational formulation for measuring information content and divergence between probability distributions.

Mutual Information in Biological Data

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

Information Measures: Estimation & Detection

Covers information measures, entropy, mutual information, and data processing inequality in signal representation.

Probability Theory: Joint Marginals and Granger Causality

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

Information Theory: Review and Mutual Information

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Protein Residue Coevolution Analysis

Delves into analyzing residue coevolution in protein families to capture native contacts and predict spatial proximity and protein interactions.

Maximal Correlation: Information Measures

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

Information Measures: Entropy and Information Theory

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

Mutual Information and Entropy

Explores mutual information and entropy calculation between random variables.

Generalization Error

Discusses mutual information, data processing inequality, and properties related to leakage in discrete systems.

Derivability and Continuity

Explores derivability and continuity of functions, showcasing examples of functions with differentiability properties.

Structured Learning: Chau-Lieu Algorithm

Explores structured learning with the Chau-Lieu algorithm and delves into the basics of deep learning and neural networks.

Mutual Information: Understanding Random Variables

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

Data Handling: Problems and Distributions

Covers common data problems and important distributions, along with correlation and dependencies analysis.

Information Measures: Part 1

Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.

Information Measures

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

Encoder for Force and Vision in Manipulation

Explores combining force and vision for efficient manipulation tasks using a lower dimensional representation.

Achievable Rate & Capacity

Explores achievable rate, channel capacity, spectral efficiency, and fading channels in wireless communication systems.

Information Theory and Coding

Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.