Standard syllabus
Statistical learning · Undergraduate · Statistics
Topics
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
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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