Prerequisites: Python & ML Fundamentals
Level:
Statistical Fidelity Assessment
Apply advanced statistical methods to compare distributions between real and synthetic datasets.
Machine Learning Utility Evaluation
Quantify the usefulness of synthetic data for training downstream machine learning models.
Privacy Risk Quantification
Implement techniques to assess the privacy leakage risks associated with synthetic datasets.
Generative Model Specific Metrics
Utilize metrics tailored for evaluating the output of specific generative models (GANs, VAEs, etc.).
Domain-Specific Evaluation
Adapt evaluation strategies for specialized data types like time-series or sequential data.
Implementation of Evaluation Pipelines
Build automated pipelines for comprehensive synthetic data quality reporting.