Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Explores stationarity in stochastic processes, showcasing how statistical characteristics remain constant over time and the implications on random variables and Fourier transforms.
Explores portfolio rate of return, valuation, risk characterization, and historical performance, emphasizing diversification benefits and mean-variance analysis.
Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.
