Clustering-K means, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Models, Association Rule Learning, Dimensionality Reduction, Anomaly Detection, Principal Component Analysis (PCA), SVD, Autoencoders, Variational Autoencoders, Generative Adversarial Networks, Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs), SelfOrganizing Maps (SOMs), Hierarchical Temporal Memory (HTM), use cases: customer segmentation, image recognition, anomaly detection, scientific discovery. Semi-supervised learning, Gaussian mixtures, manifold learning, decomposing signals using matrix factorization problems, density estimation