Linear model

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Introduces regularization techniques to prevent overfitting in machine learning models.

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Emphasizes experimental design in genomic data analysis, addressing technical variability, batch effects, and statistical solutions.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Explores the optimality of the Least Squares Estimator and its large sample distribution.

Explores ARCH and GARCH models, volatility clustering, time series, estimation, and filtering steps in financial and macroeconomic contexts.