Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Explores the concept of independence in probability theory, showing how events can occur without influencing each other.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
