Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
