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machine-learning

Simple linear regression and least squares

taught

Fitting a line to data by minimizing the sum of squared residuals.

Taught in

Requires

Required by

Local knowledge map

Local knowledge map

From logistic regression to a neural network Generative classifiers: LDA and QDA Multiclass logistic regression Probability calibration Regression evaluation: R-squared, MAE, MSE, RMSE Binary logistic regression Multiple linear regression Simple linear regression and least squares Backpropagation: the chain rule you already know Feature creation: polynomial, interaction, binning,… Features, samples, and training/validation/test splits Models, training, inference, and prediction Multiple regression and the design matrix PCA: variance maximization and reconstruction error Probability vocabulary for ML: Bayes' theorem,… Scaling and standardization The maximal margin classifier Data as vectors and matrices Optimization vocabulary for ML: gradient descent and… Convex functions and epigraphs Lagrange multipliers Matrix-vector multiplication two ways: the row picture… Stochastic gradient descent

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Solid arrows are required prerequisites; dashed are recommended or helpful. Lower rows are more foundational. Click any box to jump to that concept.