Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
