Maximum Likelihood InferenceExplores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
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.
Discrete Choice AnalysisIntroduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Exponential FamilyCovers the properties of the exponential family and the estimation of parameters.
Model Selection: AIC and BICExplores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.
