COM SCI C260F · CS C260FFoundation Models: Principles and Practice
Computer Science · 4 units · Graduate courses (200-299)
Discussion of advanced topics and state-of-the-art research in training foundation models, including large language models (LLMs) and large vision language models (LVLMs). Topics include pretraining and post-training foundation models, safety alignment, training reasoning LLMs and VLMs, and synthetic data generation for foundation models. Concurrently scheduled with course C160F.
Letter grading.
When it runs
- Winter 2027
Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.
Requisites
Official UCLA wording
Requisite: course M146.
BruinTree reads · Prerequisite
confidence 1.00 · from UCLA’s structured dataRequires
Everything that has to come before this course, not just the courses named in the requisite above.
COM SCI C260F
- 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
- COMPTNG 10CAdvanced Programming
- EC ENGR 131AProbability and Statistics
- MATH 33ALinear Algebra and Applications
- STATS 100AIntroduction to Probability
- MATH 170AProbability Theory I
- MATH 131AAnalysis
- MATH 170EIntroduction to Probability and Statistics 1: Probability
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.
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No course in the catalog lists this as a requisite.