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Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Interval EstimationCovers the construction of confidence intervals for a normal distribution with unknown mean and variance.
Testing: t-testsCovers t-tests, p-values calculation, and comparison of coefficients.
Detection & EstimationCovers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Optimality in Statistical InferenceDelves into the duality between confidence intervals and hypothesis tests, emphasizing the importance of precision and accuracy in estimation.
Dependence and CorrelationExplores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.