Lectures related to Information Theory: Entropy and Information Processing

Covers information measures like entropy and Kullback-Leibler divergence.

Covers the basics of probability theory, random variables, joint probability, and inference.

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Quantifying Randomness and Information in Biological Data

Explores entropy, randomness, and information quantification in biological data analysis, including neuroscience and protein structure prediction.

Information Measures

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

Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Data Compression and Entropy Definition

Explores the concept of entropy as the average number of questions needed to guess a randomly chosen letter in a sequence, emphasizing its enduring relevance in information theory.