Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.
