Dynamic Portfolio ChoiceExplores dynamic portfolio choice with time-varying opportunities and transaction costs, providing optimal strategies and empirical insights.
PCA: Key ConceptsCovers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Air Pollution: Correlation AnalysisCovers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
PCA: Key ConceptsCovers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Portfolio Management: Risk and ReturnExplores portfolio rate of return, valuation, risk characterization, and historical performance, emphasizing diversification benefits and mean-variance analysis.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Multivariate Methods IExplores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.