Learning Strategies and Rewards

Related lectures (31)

Page 3 of 4

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Delves into high-order correlations in massive datasets and their importance.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Explores statistical independence, correlation, Gauss functions, likelihood estimation, and Gaussian Mixture Models for clustering.

Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.

Explores the curse of dimensionality, variable selection methods, coefficient of determination, and limitations of filtering techniques.

Explores correlation inequalities and their applications in various contexts.

Delves into complex systems, strange attractors, and epidemics, emphasizing the influence of psychological factors and the role of contagion.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Explores copulas, rank correlations, and tail dependence measures in risk management.