Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Covers examples of probability, including Bayes Theorem, independence, and conditional probability.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Explores the design of immunogenic vaccines, boosting immune response through various methods, and addressing challenges in vaccine development.
