Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Clustering & Density EstimationCovers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Detection & EstimationCovers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Maximum Likelihood InferenceExplores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Clustering: k-meansExplains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.