Fall 2026 · Lecture 15 · October 21, 2026
Autoencoders and variational autoencoders
Learning latent representations with autoencoders and generating data with variational autoencoders.
Learning goals
- Explain encoder-decoder architectures and their connection to PCA.
- Describe probabilistic latent representations in variational autoencoders.
- Understand reconstruction loss, KL divergence, and generative sampling.
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.