Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Covers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
