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Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Explores clustering methods for partitioning data into meaningful classes when labeling is unknown, covering K-means, dissimilarity measures, and hierarchical clustering.
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Explores cluster analysis methods and applications in genomic data analysis, covering classification, gene expression clustering, visualization, distance metrics, and clustering algorithms.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
