Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Neural nets and transformers; Some unsupervised learning: principal components and clustering (k-means and hierarchical).
3 units · Letter or Credit/No Credit
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Neural nets and transformers; Some unsupervised learning: principal components and clustering (k-means and hierarchical).
Offered in Winter 2027, Spring 2027 at Stanford University.