Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Delves into complex systems, strange attractors, and epidemics, emphasizing the influence of psychological factors and the role of contagion.
Covers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.
