This course provides an advanced examination of methods for assessing the quality of synthetically generated data. Learn to implement sophisticated statistical tests, evaluate model utility, assess privacy risks, and apply specialized metrics for different data types and generative models. Gain the technical proficiency to rigorously validate synthetic datasets for complex AI applications.
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.