STATS 213 · STAT 213Synthetic Data Generation
Statistics · 4 units · Graduate courses (200-299)
Introduction of data-centric approach, i.e., synthetic data generation, to build trustworthy artificial intelligence systems. In general, well-designed generation process of synthetic data can remove individual information (e.g., preserved data privacy), inject knowledge (e.g., guaranteed robustness), or increase diversity (e.g., enhanced fairness) based on raw data sets. Study includes tutorial on modern generative modeling approaches for synthetic data: generative-adversarial-network-based methods, diffusion process-based methods, and generative-flow-network-based methods. Examination of several use cases of synthetic data in various industries including financial service, e-commerce, and health care.
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.
Requisites
Official UCLA wording
Requisite: one course from course 200B, 201B, 202A, M231A, 231B.
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.
STATS 213
- STATS 200BTheoretical Statistics
- STATS 201BStatistical Modeling and Learning
- STATS 200AApplied Probability
- STATS 201AResearch Design, Sampling, and Analysis
- STATS 202AStatistics Programming
- STATS 231BMethods of Machine Learning
- STATS 208Statistical Learning Theory
- STATS M231APattern Recognition and Machine Learning
5 direct requisites. Showing 11 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.