Covers the concept of distribution estimation and the optimization of parameters using different estimators.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
