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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 multinomial distribution, joint density, marginal distribution, and conditional distribution.
Covers conditional expectation, Fubini's theorem, and their applications in probability theory.
Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.
Covers examples of probability, including Bayes Theorem, independence, and conditional probability.
