Explores autocorrelation, periodicity, and spurious correlations in time series data, emphasizing the importance of understanding underlying processes and cautioning against misinterpretation.
Explores the challenges of robust vision, including distribution shifts, failure examples, and strategies for improving model robustness through diverse data pretraining.
Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.
Covers basic probability theory, ANOVA, experimental design, and correlations, emphasizing the importance of planning multiple tests and power analysis.
Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Delves into the relationships between mood disorders, cognitive performance, and brain plasticity in urban environments, using data from medical cohorts.
