Course Outline

Introduction to Generative AI

  • Defining generative AI
  • Overview of generative models (GANs, VAEs, etc.)
  • Applications and case studies

The Need for Synthetic Data

  • Limitations of real data
  • Privacy and security concerns
  • Enhancing AI model robustness

Generating Synthetic Data

  • Techniques for synthetic data generation
  • Ensuring data quality and diversity
  • Practical workshop: Creating your first synthetic dataset

Evaluating Synthetic Data

  • Metrics for assessing synthetic data quality
  • Comparing synthetic vs. real data performance
  • Case study analysis

Ethical and Legal Aspects

  • Navigating the ethical landscape
  • Legal frameworks and compliance
  • Balancing innovation with responsibility

Advanced Topics in Data Synthesis

  • Synthetic data for unsupervised learning
  • Cross-domain data synthesis
  • Future trends in generative AI

Capstone Project

  • Applying knowledge to real-world scenarios
  • Developing a synthetic data strategy
  • Assessment and feedback

Summary and Next Steps

Requirements

  • An understanding of basic machine learning concepts
  • Experience with Python programming
  • Familiarity with data science workflows

Audience

  • Data scientists
  • AI practitioners
 21 Hours

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