Supervised Classical
4concepts
32flashcards
30minutes of reading
- 01 Linear Regression as Projection Least squares is orthogonal projection onto the column space of the design matrix, which explains the normal equations, the meaning of residuals, and why nobody who ships numerical code inverts the matrix.
- 02 Logistic Regression and the Log-Odds Why classification is modelled on the log-odds scale rather than the probability scale, why there is no closed-form solution, and what perfect separation does to the coefficients.
- 03 Ridge, Lasso and Elastic Net Why an $\ell_1$ penalty produces exact zeros while $\ell_2$ only shrinks, what each does to correlated features, and why regularisation is a bias-variance trade rather than a way to fix a bad model.
- 04 Margins, Kernels and the Support Vector Machine How maximising the distance to the nearest point gives a classifier that depends on a handful of examples, and how the dual formulation lets you work in an infinite-dimensional feature space without ever visiting it.