Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Linear Algebra: DiagonalizationExplores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.
Maximum Likelihood InferenceExplores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Linear Algebra: Canonical BasisExplores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.
Handling Network DataExplores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
