Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
