Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers t-tests, p-values calculation, and comparison of coefficients.
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.
Covers the properties of the exponential family and the estimation of parameters.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
