Time Series ClusteringCovers clustering time series data using dynamic time warping, string metrics, and Markov models.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Dimensionality Reduction: PCA & LDACovers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Data Visualization & StorytellingDelves into data physicalization, expressiveness, feminist visualization, and the balance between exploration and explanation in data visualization.