Fall 2026 · Lecture 2 · August 26, 2026
Clustering and K-means
Discovering groups in unlabeled data through distance, assignment, and iterative refinement.
Learning goals
- Express the K-means objective.
- Explain the assignment and centroid-update steps.
- Recognize sensitivity to scaling, initialization, and cluster shape.
Materials
Lecture notes
A geometric explanation, small worked example, and practical implementation notes 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.