- Introduction to course
- Overview of supervised learning
- Linear methods for regression (and classification)
- Model assessment and selection
- Basis expansions
- Kernel smoothing methods
- Additive models, trees and bagging
- Random forests
- Boosting and additive trees
- Neural networks
- Conformal prediction <- Note: not included in the exam.
Lecture slides
Published Aug. 20, 2025 8:56 AM
- Last modified Nov. 27, 2025 1:06 PM