Fall 2026 · Lecture 10 · September 28, 2026
Gaussian process regression
Bayesian prediction over functions with kernels and calibrated uncertainty.
Learning goals
- Define a Gaussian process through mean and covariance functions.
- Describe the predictive distribution.
- Interpret posterior mean and uncertainty.
Materials
Lecture notes
A function-space visual and prediction walkthrough 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.