Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Delves into entropy in neuroscience data and ecology, exploring the representation of sensory information and the diversity of biological populations.
