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Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
