STEM / applied
Statistical learning · Undergraduate · Math
Learning objectives from the Statistical learning syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Statistical learning — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Linear methods for prediction
- Linear regression as a learning method — Linear regression as a learning method
- Subset selection and shrinkage: ridge and lasso — Subset selection and shrinkage: ridge and lasso
- Bias-variance tradeoff — Bias-variance tradeoff
- Cross-validation — Cross-validation
- Model selection — Model selection
- Polynomial and spline regression — Polynomial and spline regression
Classification and beyond
- Logistic regression — Logistic regression
- Linear discriminant analysis — Linear discriminant analysis
- Support vector machines (introduction) — Support vector machines (introduction)
- Decision trees and random forests — Decision trees and random forests
- Neural networks overview (optional) — Neural networks overview (optional)
- Unsupervised learning: clustering and PCA — Unsupervised learning: clustering and PCA
Theory and diagnostics
- Overfitting — Overfitting
- Regularization paths — Regularization paths
- Resampling methods: bootstrap and CV — Resampling methods: bootstrap and CV
- Model interpretation: partial dependence (intro) — Model interpretation: partial dependence (intro)
- Statistical learning vs classical inference — Statistical learning vs classical inference
Learning objectives
Click an objective for study materials.
Linear methods for prediction
- Linear regression as a learning method — Linear regression as a learning method
- Subset selection and shrinkage: ridge and lasso — Subset selection and shrinkage: ridge and lasso
- Bias-variance tradeoff — Bias-variance tradeoff
- Cross-validation — Cross-validation
- Model selection — Model selection
- Polynomial and spline regression — Polynomial and spline regression
Classification and beyond
- Logistic regression — Logistic regression
- Linear discriminant analysis — Linear discriminant analysis
- Support vector machines (introduction) — Support vector machines (introduction)
- Decision trees and random forests — Decision trees and random forests
- Neural networks overview (optional) — Neural networks overview (optional)
- Unsupervised learning: clustering and PCA — Unsupervised learning: clustering and PCA
Theory and diagnostics
- Overfitting — Overfitting
- Regularization paths — Regularization paths
- Resampling methods: bootstrap and CV — Resampling methods: bootstrap and CV
- Model interpretation: partial dependence (intro) — Model interpretation: partial dependence (intro)
- Statistical learning vs classical inference — Statistical learning vs classical inference
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Linear methods for prediction
Linear regression as a learning method
Coming soon - Classification and beyond
Logistic regression
Coming soon - Theory and diagnostics
Overfitting
Coming soon
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