Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the properties of the exponential family and the estimation of parameters.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Delves into the duality between confidence intervals and hypothesis tests, emphasizing the importance of precision and accuracy in estimation.
Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
