Maximum Likelihood InferenceExplores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Detection & EstimationCovers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Statistical InferenceCovers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
Discrete Choice AnalysisIntroduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Eigenvalues and EM AlgorithmCovers eigenvectors, principal components, likelihood variables, EM algorithm, Jensen's inequality, and maximizing lower bounds.
Supervised Learning EssentialsIntroduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
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.
