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Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Covers the principles and methods of clustering in machine learning, including similarity measures, PCA projection, K-means, and initialization impact.
