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 the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.
