Distances and Motif CountsExplores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Network clusteringExplores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.
Graph Coloring IIExplores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.