Explores dynamic portfolio choice with time-varying opportunities and transaction costs, providing optimal strategies and empirical insights.
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
