Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Clustering: k-meansExplains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Clustering & Density EstimationCovers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Clustering: K-MeansCovers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Topic ModelsIntroduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Graph Coloring IIExplores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Clustering MethodsCovers K-means, hierarchical, and DBSCAN clustering methods with practical examples.