Fall 2026 · Lecture 13 · October 12, 2026
Bias-variance tradeoff
Understanding generalization through bias, variance, and noise, and mitigating overfitting.
Learning goals
- Distinguish empirical risk from expected prediction loss.
- Explain the bias-variance decomposition and its connection to model complexity.
- Describe methods to detect and mitigate overfitting.
Materials
Lecture notes
Additional notes and examples will be added here.
Lecture extensions
Course materials and optional topics
Four independently reviewed sections
Assignment pending
Develop the assigned part of the lecture’s core course materials.
The course team will assign this section through a GitHub issue.
Assignment pending
Develop the assigned part of the lecture’s core course materials.
The course team will assign this section through a GitHub issue.
Assignment pending
Develop an optional extension, application, or related topic assigned for this lecture.
The course team will assign this section through a GitHub issue.
Assignment pending
Develop an optional extension, application, or related topic assigned for this lecture.
The course team will assign this section through a GitHub issue.