COM SCI 261

Deep Generative Models

Computer Science · 4 units · Graduate courses (200-299)

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Fundamentals of variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow models, energy-based models, diffusion models. Applications of generative models in reinforcement learning, scientific discovery, and societal challenges in high-stakes deployments.

S/U or letter grading.

When it runs

Not on the schedule for any of Fall 2025 through Spring 2027. UCLA publishes only that window, so this does not mean the course is gone — check the official listing.

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Requisites

Official UCLA wording
Requisite: course M146.

BruinTree reads · Prerequisite

confidence 1.00 · from UCLA’s structured data

COM SCI M146

Requires

Everything that has to come before this course, not just the courses named in the requisite above.

COM SCI 261

COM SCI M146Introduction to Machine Learning
- COM SCI 32Introduction to Computer Science II
  - COM SCI 31Introduction to Computer Science I
- C&EE 110Introduction to Probability and Statistics for Engineers
  - MATH 32ACalculus of Several Variables1 more beneath
  - MATH 33ALinear Algebra and Applicationsanother path to it
- COMPTNG 10C

1 direct requisite. Showing 24 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.

Unlocks

What this course is a requisite for, and what those courses lead to in turn.

No course in the catalog lists this as a requisite.