Maximal Correlation: Information Measures

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Information Theory and Coding

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

Interpretation of Entropy

Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.

Protein Residue Coevolution Analysis

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

Quantum Information: Density Matrices

Explores density matrices, quantum states representation, and entropy in quantum information.

Probability Theory: Joint Marginals and Granger Causality

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

Information Theory: Entropy and Information Processing

Explores entropy in information theory and its role in data processing and probability distributions.

Information Theory: Quantifying Messages and Source Entropy

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

Quantifying Statistical Dependence: Covariance and Correlation

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

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.

Entropy: Examples and Properties

Explores examples of guessing letters, origins of entropy, and properties in information theory.