Classical Supervised Learning
Linear and logistic regression, regularisation, margins, kernels, and the geometry underneath them.
4concepts
64flashcards
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.