Stepwise regression

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Explores LASSO regression for inducing sparsity in signals through gradient descent.

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

Covers PhD programs at EPFL, including selection procedures, research areas, and support for students.

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Covers variational inference and neural networks for classification tasks.

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.