Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Probability and StatisticsIntroduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Conditional ProbabilityExplores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Introduction to Probability TheoryCovers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Probability: ExamplesCovers examples of probability, including Bayes Theorem, independence, and conditional probability.
Dependence and CorrelationExplores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Probability and StatisticsCovers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Continuous Random VariablesExplores continuous random variables, density functions, joint variables, independence, and conditional densities.
Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
