Model EvaluationDelves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.
Spike Wigner ModelExplores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.
Detection & EstimationCovers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Model Selection: Least SquaresExplores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Model Building: Linear RegressionExplores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.