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.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
