Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
