Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
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.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
