Lecture
Advanced Machine Learning: Ridge Regression
Description
This lecture covers the basics of linear regression, including the linear map between input and output, centering the data, the least-square estimate, the loss function, the closed-form solution, singularity, and regularizing techniques such as introducing penalty for large weights. It also introduces the kernel trick to enable nonlinear regression.
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