COGS 205C
Introduction to Statistical Learning
Introduction to statistical learning for scientific research. Topics include linear regression, logistic regression, model selection, cross-validation, ridge and Lasso regularization, classification trees, ensemble methods, random forests, unsupervised learning, PCA, k-means clustering, Gaussian mixture models, and PLS regression.
Topics
- linear regression
- logistic regression
- model selection
- cross-validation
- ridge and Lasso regularization
- classification trees
- ensemble methods
- random forests
- unsupervised learning
- k-means clustering
- Gaussian mixture models
- and PLS regression
Topics explicitly listed in official catalogue description
Prerequisites & course information
Introduction to statistical learning for scientific research. Topics include linear regression, logistic regression, model selection, cross-validation, ridge and Lasso regularization, classification trees, ensemble methods, random forests, unsupervised learning, PCA, k-means clustering, Gaussian mixture models, and PLS regression.
Sources
- Course catalogue ↗2026-2027