Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
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, 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 basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Covers examples of probability, including Bayes Theorem, independence, and conditional probability.
Explores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.
