Statistical learning
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Linear methods for prediction
- Linear regression as a learning method
- Subset selection and shrinkage: ridge and lasso
- Bias-variance tradeoff
- Cross-validation and model selection
- Polynomial and spline regression
Classification and beyond
- Logistic regression and linear discriminant analysis
- Support vector machines (introduction)
- Decision trees and random forests
- Neural networks overview (optional)
- Unsupervised learning: clustering and PCA
Theory and diagnostics
- Overfitting and regularization paths
- Resampling methods: bootstrap and CV
- Model interpretation: partial dependence (intro)
- Statistical learning vs classical inference
STEM / applied
Applied statistical learning
- Implementing methods in R (glmnet, caret) or Python
- Tuning lasso and elastic net models
- Comparing learners on benchmark datasets
- Communicating predictive performance to stakeholders
- Reproducible modeling with tidymodels or sklearn
- Case studies in genomics, finance, and marketing
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Often based on Hastie, Tibshirani, and Friedman at an accessible level. More statistical than CS machine learning courses.