K-means AlgorithmCovers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Clustering MethodsCovers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Graph Coloring IIExplores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Clustering & Density EstimationCovers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Time Series ClusteringCovers clustering time series data using dynamic time warping, string metrics, and Markov models.