Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
