Kernel K-means ClusteringExplores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
DNA: PCR and Sequencing TechniquesExplores DNA engineering through PCR, Sanger sequencing, the Human Genome Project, recombinant DNA, bacterial DNA manipulation, and CRISPR/Cas9.
Cluster Analysis: Methods and ApplicationsExplores cluster analysis methods and applications in genomic data analysis, covering classification, gene expression clustering, visualization, distance metrics, and clustering algorithms.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
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.
Clustering: k-meansExplains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.