Functional Linear Regression: Sparse Estimation and Adaptive Methods

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.