Lectures related to Canonical Correlation Analysis

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.

Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.

Explores variance, covariance, and practical applications in statistics and probability.

Explores fundamental solutions in partial differential equations, highlighting their significance in mathematical applications.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.